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Schouten TJ, van Goor IWJM, Dorland GA, Besselink MG, Bonsing BA, Bosscha K, Brosens LAA, Busch OR, Cirkel GA, van Dam RM, Festen S, Groot Koerkamp B, van der Harst E, de Hingh IHJT, Intven MPW, Kazemier G, Liem MSL, van Lienden KP, Los M, de Meijer VE, Patijn GA, Schreinemakers JMJ, Stommel MWJ, van Tienhoven GJ, Verdonk RC, Verkooijen HM, van Santvoort HC, Molenaar IQ, Daamen LA. The Value of Biological and Conditional Factors for Staging of Patients with Resectable Pancreatic Cancer Undergoing Upfront Resection: A Nationwide Analysis. Ann Surg Oncol 2024; 31:4956-4965. [PMID: 38386198 PMCID: PMC11236903 DOI: 10.1245/s10434-024-15070-w] [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: 08/30/2023] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Novel definitions suggest that resectability status for pancreatic ductal adenocarcinoma (PDAC) should be assessed beyond anatomical criteria, considering both biological and conditional factors. This has, however, yet to be validated on a nationwide scale. This study evaluated the prognostic value of biological and conditional factors for staging of patients with resectable PDAC. PATIENTS AND METHODS A nationwide observational cohort study was performed, including all consecutive patients who underwent upfront resection of National Comprehensive Cancer Network resectable PDAC in the Netherlands (2014-2019) with complete information on preoperative carbohydrate antigen (CA) 19-9 and Eastern Cooperative Oncology Group (ECOG) performance status. PDAC was considered biologically unfavorable (RB+) if CA19-9 ≥ 500 U/mL and favorable (RB-) otherwise. ECOG ≥ 2 was considered conditionally unfavorable (RC+) and favorable otherwise (RC-). Overall survival (OS) was assessed using Kaplan-Meier and Cox-proportional hazard analysis, presented as hazard ratios (HRs) with 95% confidence interval (CI). RESULTS Overall, 688 patients were analyzed with a median overall survival (OS) of 20 months (95% CI 19-23). OS was 14 months (95% CI 10 months-median not reached) in 20 RB+C+ patients (3%; HR 1.61, 95% CI 0.86-2.70), 13 months (95% CI 11-15) in 156 RB+C- patients (23%; HR 1.86, 95% CI 1.50-2.31), and 21 months (95% CI 12-41) in 47 RB-C+ patients (7%; HR 1.14, 95% CI 0.80-1.62) compared with 24 months (95% CI 22-27) in 465 patients with RB-C- PDAC (68%; reference). CONCLUSIONS Survival after upfront resection of anatomically resectable PDAC is worse in patients with CA19-9 ≥ 500 U/mL, while performance status had no impact. This supports consideration of CA19-9 in preoperative staging of resectable PDAC.
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Affiliation(s)
- Thijs J Schouten
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands
| | - Iris W J M van Goor
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands
- Department of Radiation Oncology, University Medical Center Utrecht Cancer Center, Utrecht, The Netherlands
| | - Galina A Dorland
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands
| | - Marc G Besselink
- Amsterdam UMC, Department of Surgery, University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Bert A Bonsing
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Koop Bosscha
- Department of Surgery, Jeroen Bosch Hospital, Den Bosch, The Netherlands
| | - Lodewijk A A Brosens
- Department of Pathology, University Medical Center Utrecht Cancer Center, Utrecht, The Netherlands
| | - Olivier R Busch
- Amsterdam UMC, Department of Surgery, University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Geert A Cirkel
- Department of Medical Oncology, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht, The Netherlands
- Department of Medical Oncology, Meander Medical Center, Amersfoort, The Netherlands
| | - Ronald M van Dam
- Department of Surgery, Maastricht UMC+,, Maastricht, The Netherlands
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of General and Visceral Surgery, University Hospital Aachen, Aachen, Germany
| | | | - Bas Groot Koerkamp
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | | | - Ignace H J T de Hingh
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands
| | - Martijn P W Intven
- Department of Radiation Oncology, University Medical Center Utrecht Cancer Center, Utrecht, The Netherlands
| | - Geert Kazemier
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, VU University, Amsterdam, The Netherlands
| | - Mike S L Liem
- Department of Surgery, Medical Spectrum Twente, Enschede, The Netherlands
| | - Krijn P van Lienden
- Department of Interventional Radiology, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht, The Netherlands
| | - Maartje Los
- Department of Medical Oncology, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht, The Netherlands
| | - Vincent E de Meijer
- Department of Surgery, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Gijs A Patijn
- Department of Surgery, Isala Clinics, Zwolle, The Netherlands
| | | | - Martijn W J Stommel
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Jan van Tienhoven
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC, Department of Radiation Oncology, location University of Amsterdam, Amsterdam, The Netherlands
| | - Robert C Verdonk
- Department of Gastroenterology, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht, The Netherlands
| | - Helena M Verkooijen
- Imaging Division, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hjalmar C van Santvoort
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands
| | - I Quintus Molenaar
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands
| | - Lois A Daamen
- Department of Surgery, Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center and St. Antonius Hospital Nieuwegein, Utrecht University, Utrecht, The Netherlands.
- Imaging Division, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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Bouloubasi Z, Karayiannis D, Pafili Z, Almperti A, Nikolakopoulou K, Lakiotis G, Stylianidis G, Vougas V. Re-assessing the role of peri-operative nutritional therapy in patients with pancreatic cancer undergoing surgery: a narrative review. Nutr Res Rev 2024; 37:121-130. [PMID: 37668101 DOI: 10.1017/s0954422423000100] [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] [Indexed: 09/06/2023]
Abstract
Pancreatic cancer is the most common medical condition that requires pancreatic resection. Over the last three decades, significant improvements have been made in the conditions and procedures related to pancreatic surgery, resulting in mortality rates lower than 5%. However, it is important to note that the morbidity in pancreatic surgery remains r latively high, with a percentage range of 30-60%. Pre-operative malnutrition is considered to be an independent risk factor for post-operative complications in pancreatic surgery, such as impaired wound healing, higher infection rates, prolonged hospital stay, hospital readmission, poor prognosis, and increased morbidity and mortality. Regarding the post-operative period, it is crucial to provide the best possible management of gastrointestinal dysfunction and to handle the consequences of alterations in food digestion and nutrient absorption for those undergoing pancreatic surgery. The European Society for Clinical Nutrition and Metabolism (ESPEN) suggests that early oral feeding should be the preferred way to initiate nourishing surgical patients as it is associated with lower rates of complications. However, there is ongoing debate about the optimal post-operative feeding approach. Several studies have shown that enteral nutrition is associated with a shorter time to recovery, superior clinical outcomes and biomarkers. On the other hand, recent data suggest that nutritional goals are better achieved with parenteral feeding, either exclusively or as a supplement. The current review highlights recommendations from existing evidence, including nutritional screening and assessment and pre/post-operative nutrition support fundamentals to improve patient outcomes. Key areas for improvement and opportunities to enhance guideline implementation are also highlighted.
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Affiliation(s)
- Zoi Bouloubasi
- Department of Clinical Nutrition, Evangelismos General Hospital, Athens, Greece
| | | | - Zoe Pafili
- Department of Clinical Nutrition, Evangelismos General Hospital, Athens, Greece
| | - Avra Almperti
- Department of Clinical Nutrition, Evangelismos General Hospital, Athens, Greece
| | | | - Grigoris Lakiotis
- 2nd Department of Surgery, Evangelismos General Hospital, Athens, Greece
| | - George Stylianidis
- 2nd Department of Surgery, Evangelismos General Hospital, Athens, Greece
| | - Vasilios Vougas
- 1st Department of Surgery and Transplantation, Evangelismos General Hospital, Athens, Greece
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3
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Wang G, Lei W, Duan S, Cao A, Shi H. Preoperative evaluating early recurrence in resectable pancreatic ductal adenocarcinoma by using CT radiomics. Abdom Radiol (NY) 2024; 49:484-491. [PMID: 37955726 DOI: 10.1007/s00261-023-04074-x] [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: 07/10/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVE To investigate the feasibility of a radiomics model based on contrast-enhanced CT for preoperatively predicting early recurrence after curative resection in patients with resectable pancreatic ductal adenocarcinoma (PDAC). METHODS One hundred and eighty-six patients with resectable PDAC who underwent curative resection were included and allocated to training set (131 patients) and validation set (55 patients). Radiomics features were extracted from arterial phase and portal venous phase images. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature construction. The radiomics model based on radiomics signature and clinical features was developed by the multivariate logistic regression analysis. Performance of the radiomics model was investigated by the area under the receiver operating characteristic (ROC) curve. RESULTS The radiomics signature, consisting of three arterial phase and three venous phase features, showed optimal prediction performance for early recurrence in both training (AUC = 0.73) and validation sets (AUC = 0.66). Multivariate logistic analysis identified the radiomics signature (OR, 2.58; 95% CI 2.36-3.17; p = 0.002) and clinical stage (OR, 1.60; 95% CI 1.15-2.30; p = 0.007) as independent predictors. The AUC values for risk evaluation of early recurrence using the radiomics model incorporating clinical stage were 0.80 (training set) and 0.75 (validation set). CONCLUSION The radiomics-based model integrating with clinical stage can predict early recurrence after upfront surgery in patients with resectable PDAC.
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Affiliation(s)
- Gang Wang
- Department of Radiotherapy, The Second Affiliated Hospital of Xuzhou Medical University, 32 Meijian Road, Xuzhou, People's Republic of China
| | - Weijie Lei
- Department of Radiotherapy, The Second Affiliated Hospital of Xuzhou Medical University, 32 Meijian Road, Xuzhou, People's Republic of China
| | - Shaofeng Duan
- GE Healthcare, Pudong New Town, 1 Huatuo Road, Shanghai, People's Republic of China
| | - Aihong Cao
- Department of Radiology, The Second Affiliated Hospital of Xuzhou Medical University, 32 Meijian Road, Xuzhou, People's Republic of China.
| | - Hongyuan Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, People's Republic of China.
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Pacella G, Brunese MC, D’Imperio E, Rotondo M, Scacchi A, Carbone M, Guerra G. Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis. J Clin Med 2023; 12:7380. [PMID: 38068432 PMCID: PMC10707069 DOI: 10.3390/jcm12237380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 09/10/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. METHODS The PubMed database was searched for papers published in the English language no earlier than January 2018. RESULTS We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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Affiliation(s)
- Giulia Pacella
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Maria Chiara Brunese
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | | | - Marco Rotondo
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Andrea Scacchi
- General Surgery Unit, University of Milano-Bicocca, 20126 Milan, Italy
| | - Mattia Carbone
- San Giovanni di Dio e Ruggi d’Aragona Hospital, 84131 Salerno, Italy;
| | - Germano Guerra
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
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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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6
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Wu HY, Li JW, Li JZ, Zhai QL, Ye JY, Zheng SY, Fang K. Comprehensive multimodal management of borderline resectable pancreatic cancer: Current status and progress. World J Gastrointest Surg 2023; 15:142-162. [PMID: 36896309 PMCID: PMC9988647 DOI: 10.4240/wjgs.v15.i2.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/23/2022] [Accepted: 01/12/2023] [Indexed: 02/27/2023] Open
Abstract
Borderline resectable pancreatic cancer (BRPC) is a complex clinical entity with specific biological features. Criteria for resectability need to be assessed in combination with tumor anatomy and oncology. Neoadjuvant therapy (NAT) for BRPC patients is associated with additional survival benefits. Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT. More attention to management standards during NAT, including biliary drainage and nutritional support, is needed. Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period, including NAT responsiveness and the selection of surgical timing.
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Affiliation(s)
- Hong-Yu Wu
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Jin-Wei Li
- Department of Neurosurgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545000, Guangxi Province, China
| | - Jin-Zheng Li
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Qi-Long Zhai
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Jing-Yuan Ye
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Si-Yuan Zheng
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Kun Fang
- Department of Surgery, Yinchuan Maternal and Child Health Hospital, Yinchuan 750000, Ningxia, China
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7
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Wang F, Zhao Y, Xu J, Shao S, Yu D. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients. Front Oncol 2022; 12:1037672. [PMID: 36518321 PMCID: PMC9742428 DOI: 10.3389/fonc.2022.1037672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2023] Open
Abstract
PURPOSE To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC). METHODS 184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis. RESULTS Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort. CONCLUSION We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.
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Affiliation(s)
- Fangqing Wang
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yuxuan Zhao
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Jianwei Xu
- Department of Pancreatic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Sai Shao
- Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Dexin Yu
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, 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: 3] [Impact Index Per Article: 1.5] [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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9
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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] [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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10
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Healy GM, Salinas-Miranda E, Jain R, Dong X, Deniffel D, Borgida A, Hosni A, Ryan DT, Njeze N, McGuire A, Conlon KC, Dodd JD, Ryan ER, Grant RC, Gallinger S, Haider MA. Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation. Eur Radiol 2021; 32:2492-2505. [PMID: 34757450 DOI: 10.1007/s00330-021-08314-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/05/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model. METHODS Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses. RESULTS The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40-5.87, p < 0.001)/5.28 (95% CI 2.35-11.86, p < 0.001), respectively, along with age 1.02 (1.01-1.04, p = 0.01)/1.02 (1.00-1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543-0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496-0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524-0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities. CONCLUSION A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings. KEY POINTS • At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging. • Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters. • Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.
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Affiliation(s)
- Gerard M Healy
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | | | - Rahi Jain
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Xin Dong
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Dominik Deniffel
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
| | - Ayelet Borgida
- Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Ali Hosni
- Radiation Medicine Program, Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - David T Ryan
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
| | - Nwabundo Njeze
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
| | - Anne McGuire
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
| | - Kevin C Conlon
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jonathan D Dodd
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Edmund Ronan Ryan
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- National Surgical Centre for Pancreatic Cancer, St. Vincent's University Hospital, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Robert C Grant
- Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Steven Gallinger
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada
- Surgical Oncology Program, Hepatobiliary Pancreatic, University Health Network, Toronto, ON, Canada
| | - Masoom A Haider
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, University of Toronto, Toronto, ON, Canada.
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada.
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