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Yao X, Zhu Y, Huang Z, Wang Y, Cong S, Wan L, Wu R, Chen L, Hu Z. Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer. Quant Imaging Med Surg 2024; 14:5460-5472. [PMID: 39144023 PMCID: PMC11320501 DOI: 10.21037/qims-23-1028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 08/16/2024]
Abstract
Background Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor-sensitizing (EGFR-sensitizing) mutations exhibit a positive response to tyrosine kinase inhibitors (TKIs). Given the limitations of current clinical predictive methods, it is critical to explore radiomics-based approaches. In this study, we leveraged deep-learning technology with multimodal radiomics data to more accurately predict EGFR-sensitizing mutations. Methods A total of 202 patients who underwent both flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans and EGFR sequencing prior to treatment were included in this study. Deep and shallow features were extracted by a residual neural network and the Python package PyRadiomics, respectively. We used least absolute shrinkage and selection operator (LASSO) regression to select predictive features and applied a support vector machine (SVM) to classify the EGFR-sensitive patients. Moreover, we compared predictive performance across different deep models and imaging modalities. Results In the classification of EGFR-sensitive mutations, the areas under the curve (AUCs) of ResNet-based deep-shallow features and only shallow features from different multidata were as follows: RES_TRAD, PET/CT vs. CT-only vs. PET-only: 0.94 vs. 0.89 vs. 0.92; and ONLY_TRAD, PET/CT vs. CT-only vs. PET-only: 0.68 vs. 0.50 vs. 0.38. Additionally, the receiver operating characteristic (ROC) curves of the model using both deep and shallow features were significantly different from those of the model built using only shallow features (P<0.05). Conclusions Our findings suggest that deep features significantly enhance the detection of EGFR-sensitizing mutations, especially those extracted with ResNet. Moreover, PET/CT images are more effective than CT-only and PET-only images in producing EGFR-sensitizing mutation-related signatures.
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Affiliation(s)
- Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
| | - Yuan Zhu
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenxing Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yue Wang
- Department of PET/CT Center and Department of Thoracic Cancer I, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shan Cong
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
| | - Liwen Wan
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ruodai Wu
- Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Long Chen
- Department of PET/CT Center and Department of Thoracic Cancer I, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Bloomfield GC, Shoucair S, Nigam A, Park BU, Fishbein TM, Radkani P, Winslow ER. The utility of axial imaging among selected patients in the early postoperative period after pancreatectomy. Surgery 2024:S0039-6060(24)00475-6. [PMID: 39048330 DOI: 10.1016/j.surg.2024.06.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/24/2024] [Accepted: 06/30/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Postoperative computed tomography imaging has been shown to play an important role in avoiding failure-to-rescue. We sought to examine the impact of the timing of such imaging studies on outcomes after pancreatectomy. METHODS Patients who underwent pancreatic resection at our institution from 2017 to 2022 were reviewed retrospectively to identify those undergoing computed tomography for any indication before discharge. Patients were subdivided by the postoperative day that the first computed tomography scan was obtained: immediate (postoperative day <3), early (postoperative day 3-7), and delayed (postoperative day >7). RESULTS Of 370 patients, 110 (30%) had a computed tomography during the initial surgical stay. The 3 timing groups were similar in age, comorbidities, pathology, operative time, and number of scans. When comparing the early with the delayed group, we found that antibiotic usage, percutaneous drainage, and overall invasive interventions during surgical stay were all similar. However, those patients who were scanned in the early period had significantly shorter length of stay (17.05 vs 22.82, P = .0008) and fewer composite days hospitalized (20.1 vs 24.9, P = .01) relative to the delayed group. Importantly, early computed tomography imaging was found to be the only independent predictor of a postoperative length of stay ≤15 days on multivariate analysis. Surgical stay mortality rates were significantly lower in the early compared with delayed group (0% vs 11%, P = .02). A change in treatment was observed in 59% after computed tomography, with 15% undergoing invasive interventions, 27% treated medically, and 16% with expectant management. CONCLUSION In our cohort, patients imaged early after pancreatectomy experienced shorter hospital stays and lower inpatient mortality relative to those scanned after the first postoperative week.
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Affiliation(s)
| | | | - Aradhya Nigam
- Department of Surgery, Medstar Georgetown University Hospital, Washington, DC
| | - Byoung Uk Park
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | | | - Emily R Winslow
- University of Wisconsin School of Medicine and Public Health, Madison, WI.
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Ozaki Y, Broughton P, Abdollahi H, Valafar H, Blenda AV. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis. Cancers (Basel) 2024; 16:2448. [PMID: 39001510 PMCID: PMC11240413 DOI: 10.3390/cancers16132448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.
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Affiliation(s)
- Yousaku Ozaki
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Phil Broughton
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Hamed Abdollahi
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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Tian H, Zhang B, Zhang Z, Xu Z, Jin L, Bian Y, Wu J. DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification. J Appl Clin Med Phys 2024; 25:e14380. [PMID: 38715381 PMCID: PMC11244679 DOI: 10.1002/acm2.14380] [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: 12/20/2023] [Revised: 02/18/2024] [Accepted: 04/15/2024] [Indexed: 07/14/2024] Open
Abstract
PURPOSE The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. The intent is to offer valuable diagnostic support to clinicians in their clinical decision-making processes. METHODS The construction of the deep learning model involved utilizing a dataset comprising abdominal magnetic resonance T2-weighted images obtained from patients diagnosed with pancreatic cystic tumors at Changhai Hospital. The dataset comprised 207 patients with SCN and 93 patients with MCN, encompassing a total of 1761 images. The foundational architecture employed was DenseNet-161, augmented with a hybrid attention mechanism module. This integration aimed to enhance the network's attentiveness toward channel and spatial features, thereby amplifying its performance. Additionally, clinical features were incorporated prior to the fully connected layer of the network to actively contribute to subsequent decision-making processes, thereby significantly augmenting the model's classification accuracy. The final patient classification outcomes were derived using a joint voting methodology, and the model underwent comprehensive evaluation. RESULTS Using the five-fold cross validation, the accuracy of the classification model in this paper was 92.44%, with an AUC value of 0.971, a precision rate of 0.956, a recall rate of 0.919, a specificity of 0.933, and an F1-score of 0.936. CONCLUSION This study demonstrates that the DenseNet model, which incorporates hybrid attention mechanisms and clinical features, is effective for distinguishing between SCN and MCN, and has potential application for the diagnosis of pancreatic cystic tumors in clinical practice.
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Affiliation(s)
- Hui Tian
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Bo Zhang
- School of Medical TechnologyBinzhou PolytechnicShandongChina
| | - Zhiwei Zhang
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Zhenshun Xu
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Liang Jin
- Department of RadiologyHuadong HospitalFudan UniversityShanghaiChina
| | - Yun Bian
- Department of RadiologyChanghai HospitalThe Navy Military Medical UniversityShanghaiChina
| | - Jie Wu
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
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Patel H, Zanos T, Hewitt DB. Deep Learning Applications in Pancreatic Cancer. Cancers (Basel) 2024; 16:436. [PMID: 38275877 PMCID: PMC10814475 DOI: 10.3390/cancers16020436] [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: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.
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Affiliation(s)
- Hardik Patel
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - Theodoros Zanos
- Northwell Health—The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA;
| | - D. Brock Hewitt
- Department of Surgery, NYU Grossman School of Medicine, New York, NY 10016, USA;
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Zhang Y, Yao J, Liu F, Cheng Z, Qi E, Han Z, Yu J, Dou J, Liang P, Tan S, Dong X, Li X, Sun Y, Wang S, Wang Z, Yu X. Radiomics Based on Contrast-Enhanced Ultrasound Images for Diagnosis of Pancreatic Serous Cystadenoma. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2469-2475. [PMID: 37749013 DOI: 10.1016/j.ultrasmedbio.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/23/2023] [Accepted: 08/08/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVE The purpose of the study was to develop and validate a radiomics model by using contrast-enhanced ultrasound (CEUS) data for pre-operative differential diagnosis of pancreatic cystic neoplasms (PCNs), especially pancreatic serous cystadenoma (SCA). METHODS Patients with pathologically confirmed PCNs who underwent CEUS examination at Chinese PLA hospital from May 2015 to August 2022 were retrospectively collected. Radiomic features were extracted from the regions of interest, which were obtained based on CEUS images. A support vector machine algorithm was used to construct a radiomics model. Moreover, based on the CEUS image features, the CEUS and the combined models were constructed using logistic regression. The performance and clinical utility of the optimal model were evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity and decision curve analysis. RESULTS A total of 113 patients were randomly split into the training (n = 79) and test cohorts (n = 34). These patients were pathologically diagnosed with SCA, mucinous cystadenoma, intraductal papillary mucinous neoplasm and solid-pseudopapillary tumor. The radiomics model achieved an AUC of 0.875 and 0.862 in the training and test cohorts, respectively. The sensitivity and specificity of the radiomics model were 81.5% and 86.5% in the training cohort and 81.8% and 91.3% in the test cohort, respectively, which were higher than or comparable with that of the CEUS model and the combined model. CONCLUSION The radiomics model based on CEUS images had a favorable differential diagnostic performance in distinguishing SCA from other PCNs, which may be beneficial for the exploration of personalized management strategies.
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Affiliation(s)
- Yiqiong Zhang
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Jundong Yao
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Department of Ultrasound, First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Zhigang Cheng
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Erpeng Qi
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhiyu Han
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Jie Yu
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Jianping Dou
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Ping Liang
- Department of Interventional Ultrasound, Fifth Medical Centre, Chinese PLA Hospital, Beijing, China
| | - Shuilian Tan
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xuejuan Dong
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xin Li
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Ya Sun
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Shuo Wang
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Zhen Wang
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Xiaoling Yu
- Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China.
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7
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Li L, Zhou X, Cui W, Li Y, Liu T, Yuan G, Peng Y, Zheng J. Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT. J Cancer Res Clin Oncol 2023; 149:15469-15478. [PMID: 37642722 DOI: 10.1007/s00432-023-05329-2] [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: 06/18/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the diagnosing of breast cancer lung metastasis (BCLM) and primary lung cancer (PLC) with low-dose CT (LDCT). METHODS We retrospectively collected the LDCT images of 100 breast cancer patients with lung lesions, comprising 60 cases of BCLM and 40 cases of PLC. We proposed a fusion model that combined deep learning features extracted from ResNet18-based multi-input residual convolution network with traditional radiomics features. Specifically, the fusion model adopted a multi-region strategy, incorporating the aforementioned features from both the ITR and PTR. Then, we randomly divided the dataset into training and validation sets using fivefold cross-validation approach. Comprehensive comparative experiments were performed between the proposed fusion model and other eight models, including the intra-tumoral deep learning model, the intra-tumoral radiomics model, the intra-tumoral deep-learning radiomics model, the peri-tumoral deep learning model, the peri-tumoral radiomics model, the peri-tumoral deep-learning radiomics model, the multi-region radiomics model, and the multi-region deep-learning model. RESULTS The fusion model developed using deep-learning radiomics feature sets extracted from the ITR and PTR had the best classification performance, with the area under the curve of 0.913 (95% CI 0.840-0.960). This was significantly higher than that of the single region's radiomics model or deep learning model. CONCLUSIONS The combination of radiomics and deep learning features was effective in discriminating BCLM and PLC. Additionally, the analysis of the PTR can mine more comprehensive tumor information.
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Affiliation(s)
- Lei Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xinglu Zhou
- Department of PET/CT Center, Harbin Medical University Cancer Hospital, Harbin, 150081, China
- Department of Radiology, Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Wenju Cui
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Yingci Li
- Department of PET/CT Center, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Tianyi Liu
- Department of Pathology, Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Gang Yuan
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
| | - Yunsong Peng
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guizhou, 550002, China.
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
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Jiang J, Chao WL, Cao T, Culp S, Napoléon B, El-Dika S, Machicado JD, Pannala R, Mok S, Luthra AK, Akshintala VS, Muniraj T, Krishna SG. Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy. Biomimetics (Basel) 2023; 8:496. [PMID: 37887627 PMCID: PMC10604893 DOI: 10.3390/biomimetics8060496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/03/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65-75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.
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Affiliation(s)
- Joanna Jiang
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Troy Cao
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Bertrand Napoléon
- Department of Gastroenterology, Jean Mermoz Private Hospital, 69008 Lyon, France
| | - Samer El-Dika
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA 94305, USA
| | - Jorge D. Machicado
- Division of Gastroenterology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rahul Pannala
- Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA
| | - Shaffer Mok
- Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Anjuli K. Luthra
- Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Venkata S. Akshintala
- Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA
| | - Thiruvengadam Muniraj
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LC. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging 2023; 104:435-447. [PMID: 36967355 DOI: 10.1016/j.diii.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Sol Goldman Pancreatic Research Center, Department of Pathology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Department of Radiology, Hôpital Cochin-APHP, 75014, 75006, Paris, France, 7501475006
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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Jiang J, Chao WL, Culp S, Krishna SG. Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma. Cancers (Basel) 2023; 15:2410. [PMID: 37173876 PMCID: PMC10177524 DOI: 10.3390/cancers15092410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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Affiliation(s)
- Joanna Jiang
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Ohio State University Wexner Medical Ceter, Columbus, OH 43210, USA
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Lee TC, Angelina CL, Kongkam P, Wang HP, Rerknimitr R, Han ML, Chang HT. Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy. Diagnostics (Basel) 2023; 13:diagnostics13071289. [PMID: 37046507 PMCID: PMC10093377 DOI: 10.3390/diagnostics13071289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
Abstract
Accurate classification of pancreatic cystic lesions (PCLs) is important to facilitate proper treatment and to improve patient outcomes. We utilized the convolutional neural network (CNN) of VGG19 to develop a computer-aided diagnosis (CAD) system in the classification of subtypes of PCLs in endoscopic ultrasound-guided needle-based confocal laser endomicroscopy (nCLE). From a retrospectively collected 22,424 nCLE video frames (50 videos) as the training/validation set and 11,047 nCLE video frames (18 videos) as the test set, we developed and compared the diagnostic performance of three CNNs with distinct methods of designating the region of interest. The diagnostic accuracy for subtypes of PCLs by CNNs with manual, maximal rectangular, and U-Net algorithm-designated ROIs was 100%, 38.9%, and 66.7% on a per-video basis and 88.99%, 73.94%, and 76.12% on a per-frame basis, respectively. Our per-frame analysis suggested differential levels of diagnostic accuracy among the five subtypes of PCLs, where non-mucinous PCLs (serous cystic neoplasm: 93.11%, cystic neuroendocrine tumor: 84.31%, and pseudocyst: 98%) had higher diagnostic accuracy than mucinous PCLs (intraductal papillary mucinous neoplasm: 84.43% and mucinous cystic neoplasm: 86.1%). Our CNN demonstrated superior specificity compared to the state-of-the-art for the classification of mucinous PCLs (IPMN and MCN), with high specificity (94.3% and 92.8%, respectively) but low sensitivity (46% and 45.2%, respectively). This suggests the complimentary role of CNN-enabled CAD systems, especially for clinically suspected mucinous PCLs.
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Affiliation(s)
- Tsung-Chun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Department of Internal Medicine, School of Medicine, College of Medicine, TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Clara Lavita Angelina
- Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
| | - Pradermchai Kongkam
- Excellent Center for Gastrointestinal Endoscopy and Division of Gastroenterology, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand
- Pancreas Research Unit, Division of Hospital and Ambulatory Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Hsiu-Po Wang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei 10002, Taiwan
| | - Rungsun Rerknimitr
- Excellent Center for Gastrointestinal Endoscopy and Division of Gastroenterology, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand
| | - Ming-Lun Han
- Department of Integrated Diagnostics and Therapeutics, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Hsuan-Ting Chang
- Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
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