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Jia W, Li F, Cui Y, Wang Y, Dai Z, Yan Q, Liu X, Li Y, Chang H, Zeng Q. Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases. Acad Radiol 2024:S1076-6332(24)00221-6. [PMID: 38702214 DOI: 10.1016/j.acra.2024.04.012] [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: 02/17/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION The DLR model is an effective method for identifying the primary source of liver metastases.
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Affiliation(s)
- Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China; Shandong First Medical University, Jinan, China.
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China.
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
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Ni H, Zhou G, Chen X, Ren J, Yang M, Zhang Y, Zhang Q, Zhang L, Mao C, Li X. Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram. Bioengineering (Basel) 2023; 10:828. [PMID: 37508855 PMCID: PMC10376503 DOI: 10.3390/bioengineering10070828] [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: 06/01/2023] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.
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Affiliation(s)
- Haixu Ni
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Gonghai Zhou
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xinlong Chen
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
| | - Jing Ren
- The Reproductive Medicine Hospital of the First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Minqiang Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Yuhong Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Qiyu Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Lei Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Chengsheng Mao
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xun Li
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
- Key Laboratory of Biotherapy and Regenerative Medicine of Gansu Province, Lanzhou 730000, China
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Ouyang H, Ma W, Si T, Liu D, Chen P, Gerdtsson AS, Song J, Ni Y, Luo J, Yan Z. Systemic Chemotherapy With or Without Hepatic Arterial Infusion Chemotherapy for Liver Metastases From Pancreatic Cancer: A Propensity Score Matching Analysis. Clin Colorectal Cancer 2023; 22:111-119. [PMID: 36473779 DOI: 10.1016/j.clcc.2022.10.007] [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: 08/22/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The significance of systemic chemotherapy (SCT) combined with hepatic arterial infusion (HAI) chemotherapy in the treatment of pancreatic ductal adenocarcinoma with liver metastases (PACLM) remains unclear. Based on previous studies, this single-center propensity score matching (PSM) study aimed to explore the efficacy of SCT with or without HAI for PACLM. PATIENT AND METHODS The PSM method was used to screen 661 cases of PACLM who received SCT at Tianjin Medical University Cancer Institute and Hospital from 2001 to 2020. According to the 1:6 ratio with PSM, 385 patients were divided into the SCT+HAI group (n = 55) and the SCT group (n = 330). After a median follow-up of 49 (range 7-153) months, overall survival (OS) and survival-related prognostic factors were analyzed. RESULTS The main baseline characteristics of the SCT+HAI group and the SCT alone group were matched appropriately (P > .05). After PSM, the median OS for patients in the 2 groups was 10.6 and 7.6 months, respectively (P = .02). Multivariate analysis revealed that peritoneal metastases (P = .03), CA199 ≥ 500U/mL (P = .03), and lactate dehydrogenase (LDH) ≥ 250U/L (P = .03) were prognostic factors of poor survival, modern SCT plus HAI (P = .04) was a protective factor. CONCLUSION Our findings indicated that adequate cycles of SCT+HAI result in better survival than SCT alone in patients with PACLM. Patients with peritoneal metastases, markedly elevated CA19-9 and LDH have a poorer prognosis. The conclusion has yet to be validated in randomized controlled clinical trials.
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Affiliation(s)
- Huaqiang Ouyang
- Department of Integrative Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Weidong Ma
- National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Pancreatic Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Tongguo Si
- National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Interventional Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Donglin Liu
- Department of Mathematics, Lund University, Lund, Sweden
| | - Ping Chen
- National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Anna Sandström Gerdtsson
- Department of Immunotechnology, CREATE Health Translational Cancer Center, Lund University, Lund, Sweden
| | - Jiahong Song
- Department of Cardiology, The second hospital of Jiaxing, Jiaxing, Zhejiang, China
| | - Yue Ni
- The Nursing Department, Beijing Bo Ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Juanjuan Luo
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Zhuchen Yan
- Department of Integrative Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
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An P, Lin Y, Zhang J, Hu Y, Qin P, Ye Y, Li X, Feng G, Wang J. Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data. Technol Cancer Res Treat 2023; 22:15330338231186739. [PMID: 37464839 PMCID: PMC10363996 DOI: 10.1177/15330338231186739] [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: 01/18/2023] [Revised: 05/06/2023] [Accepted: 05/19/2023] [Indexed: 07/20/2023] Open
Abstract
Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years' follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time < 6 months), and further the training set and test set were set at a ratio of 7/3. Then logistic regression was conducted to find independent risk factors, establish predictive models, and further the models were validated. Results: The Kaplan-Meier analysis showed that age, diabetes, tumor, node, and metastasis stage, CT enhancement mode, peripancreatic lymph node swelling, nerve invasion, surgery in a top hospital, tumor size, carbohydrate antigen 19-9, carcinoembryonic antigen, Radscore 1/2/3 were the influencing factors of PBTC recurrence. The overall average survival was 7.4 months in this study. The multivariate logistic analysis confirmed that nerve invasion, surgery in top hospital, dilation of the main pancreatic duct, and Radscore 2 were independent factors affecting the mortality of PBTC (P < .05). In the test set, the combined model achieved the best predictive performance [AUC 0.944, 95% CI (0.826-0.991)], significantly superior to the clinicopathological model [AUC 0.770, 95% CI (0.615-0.886), P = .0145], and the CT radiomics model [AUC 0.883, 95% CI (0.746-0.961), P = .1311], with a good clinical net benefit confirmed by decision curve. The same results were subsequently validated on the test set. Conclusion: The diagnosis and treatment of PBTC are challenging, and survival is poor. Nevertheless, the combined model benefits the clinical management and prognosis of PBTC.
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Affiliation(s)
- Peng An
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yong Lin
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyan Zhang
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
| | - Yan Hu
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Qin
- Department of Pancreatic Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yingjian Ye
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiumei Li
- Depatment of Radiology, Hubei Clinical Research Center of Parkinson’s disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, P.R.C
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of internal medicine, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Guoyan Feng
- Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jinsong Wang
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
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Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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