1
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Davis JMK, Niazi MKK, Ricker AB, Tavolara TE, Robinson JN, Annanurov B, Smith K, Mantha R, Hwang J, Shrestha R, Iannitti DA, Martinie JB, Baker EH, Gurcan MN, Vrochides D. Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging. J Surg Oncol 2024. [PMID: 38712939 DOI: 10.1002/jso.27673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/13/2024] [Accepted: 04/28/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND OBJECTIVES Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.
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Affiliation(s)
- Joshua M K Davis
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Muhammad Khalid Khan Niazi
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Ansley B Ricker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Thomas E Tavolara
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jordan N Robinson
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Bayram Annanurov
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Kaylee Smith
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Rohit Mantha
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Jimmy Hwang
- Department of Medical Oncology, Atrium Health Carolinas Medical Center, Levine Cancer Institute, Charlotte, North Carolina, USA
| | - Ruchi Shrestha
- Department of Radiology, Atrium Health, Charlotte, North Carolina, USA
| | - David A Iannitti
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - John B Martinie
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Erin H Baker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Dionisios Vrochides
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
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2
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Park HJ, Shin K, You MW, Kyung SG, Kim SY, Park SH, Byun JH, Kim N, Kim HJ. Deep Learning-based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT. Radiology 2023; 306:140-149. [PMID: 35997607 DOI: 10.1148/radiol.220171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Deep learning (DL) may facilitate the diagnosis of various pancreatic lesions at imaging. Purpose To develop and validate a DL-based approach for automatic identification of patients with various solid and cystic pancreatic neoplasms at abdominal CT and compare its diagnostic performance with that of radiologists. Materials and Methods In this retrospective study, a three-dimensional nnU-Net-based DL model was trained using the CT data of patients who underwent resection for pancreatic lesions between January 2014 and March 2015 and a subset of patients without pancreatic abnormality who underwent CT in 2014. Performance of the DL-based approach to identify patients with pancreatic lesions was evaluated in a temporally independent cohort (test set 1) and a temporally and spatially independent cohort (test set 2) and was compared with that of two board-certified radiologists. Performance was assessed using receiver operating characteristic analysis. Results The study included 852 patients in the training set (median age, 60 years [range, 19-85 years]; 462 men), 603 patients in test set 1 (median age, 58 years [range, 18-82 years]; 376 men), and 589 patients in test set 2 (median age, 63 years [range, 18-99 years]; 343 men). In test set 1, the DL-based approach had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.94) and showed slightly worse performance in test set 2 (AUC, 0.87 [95% CI: 0.84, 0.89]). The DL-based approach showed high sensitivity in identifying patients with solid lesions of any size (98%-100%) or cystic lesions measuring 1.0 cm or larger (92%-93%), which was comparable with the radiologists (95%-100% for solid lesions [P = .51 to P > .99]; 93%-98% for cystic lesions ≥1.0 cm [P = .38 to P > .99]). Conclusion The deep learning-based approach demonstrated high performance in identifying patients with various solid and cystic pancreatic lesions at CT. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Hyo Jung Park
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Keewon Shin
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Myung-Won You
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Sung-Gu Kyung
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - So Yeon Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Seong Ho Park
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Jae Ho Byun
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Namkug Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Hyoung Jung Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
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Cellina M, Cè M, Khenkina N, Sinichich P, Cervelli M, Poggi V, Boemi S, Ierardi AM, Carrafiello G. Artificial Intellgence in the Era of Precision Oncological Imaging. Technol Cancer Res Treat 2022; 21:15330338221141793. [PMID: 36426565 PMCID: PMC9703524 DOI: 10.1177/15330338221141793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, Milano, Italy,Michaela Cellina, MD, Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milano, Italy.
| | - Maurizio Cè
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Natallia Khenkina
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Polina Sinichich
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Marco Cervelli
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Vittoria Poggi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Sara Boemi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | | | - Gianpaolo Carrafiello
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy,Radiology Department, Fondazione IRCCS Cà Granda, Milan, Italy
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4
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Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers (Basel) 2022; 14:5382. [PMID: 36358800 PMCID: PMC9657087 DOI: 10.3390/cancers14215382] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 08/01/2023] Open
Abstract
Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.
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Affiliation(s)
- Bahrudeen Shahul Hameed
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
| | - Uma Maheswari Krishnan
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Arts, Sciences, Humanities & Education (SASHE), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
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5
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Anta JA, Martínez-Ballestero I, Eiroa D, García J, Rodríguez-Comas J. Artificial intelligence for the detection of pancreatic lesions. Int J Comput Assist Radiol Surg 2022; 17:1855-1865. [PMID: 35951286 DOI: 10.1007/s11548-022-02706-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/17/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions. METHODS We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs). RESULTS We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer. CONCLUSION AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists' tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.
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Affiliation(s)
- Julia Arribas Anta
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Gastroenterology, University Hospital, 12 Octubre. Av. de Córdoba, s/n, 28041, Madrid, Spain
| | - Iván Martínez-Ballestero
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Daniel Eiroa
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Javier García
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Júlia Rodríguez-Comas
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.
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6
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Lin KW, Ang TL, Li JW. Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma. Artif Intell Med Imaging 2022; 3:21-32. [DOI: 10.35711/aimi.v3.i2.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.
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Affiliation(s)
- Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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7
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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8
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Schlanger D, Graur F, Popa C, Moiș E, Al Hajjar N. The role of artificial intelligence in pancreatic surgery: a systematic review. Updates Surg 2022; 74:417-429. [PMID: 35237939 DOI: 10.1007/s13304-022-01255-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.
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Affiliation(s)
- D Schlanger
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - F Graur
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania. .,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania.
| | - C Popa
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - E Moiș
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - N Al Hajjar
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
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Baimas-George M, Demartines N, Vrochides D. The role of disruptive technologies and approaches in ERAS®: erupting change through disruptive means. Langenbecks Arch Surg 2022. [PMID: 35083568 DOI: 10.1007/s00423-022-02450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 01/18/2022] [Indexed: 12/05/2022]
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Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021; 39:2207-2222. [PMID: 34744231 PMCID: PMC8557108 DOI: 10.1007/s00371-021-02322-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 05/07/2023]
Abstract
With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Jixiang Zhang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
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Barat M, Chassagnon G, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Soyer P. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 2021; 39:514-523. [PMID: 33550513 DOI: 10.1007/s11604-021-01098-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 12/11/2022]
Abstract
The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 27 Rue du Faubourg Saint-Jacques, Paris, France
- Université de Paris, Descartes-Paris 5, 75006, Paris, France
| | - Guillaume Chassagnon
- Department of Radiology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 27 Rue du Faubourg Saint-Jacques, Paris, France
- Université de Paris, Descartes-Paris 5, 75006, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 27 Rue du Faubourg Saint-Jacques, Paris, France
- Université de Paris, Descartes-Paris 5, 75006, Paris, France
| | - Sébastien Gaujoux
- Université de Paris, Descartes-Paris 5, 75006, Paris, France
- Department of Abdominal Surgery, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Romain Coriat
- Université de Paris, Descartes-Paris 5, 75006, Paris, France
- Department of Gastroenterology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Robert Debré Hospital, 51092, Reims, France
| | - Christophe Cassinotto
- Department of Radiology, CHU Montpellier, University of Montpellier, Saint-Éloi Hospital, 34000, Montpellier, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, Assistance Publique-Hopitaux de Paris, 27 Rue du Faubourg Saint-Jacques, Paris, France.
- Université de Paris, Descartes-Paris 5, 75006, Paris, France.
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