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Meyer HJ, Dermendzhiev T, Hetz M, Osterhoff G, Kleber C, Denecke T, Henkelmann J, Werdehausen R, Hempel G, Struck MF. Body composition parameters in initial CT imaging of mechanically ventilated trauma patients: Single-centre observational study. J Cachexia Sarcopenia Muscle 2024. [PMID: 39185615 DOI: 10.1002/jcsm.13578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/13/2024] [Accepted: 08/06/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Body composition parameters provide relevant prognostic significance in critical care cohorts and cancer populations. Published results regarding polytrauma patients are inconclusive to date. The goal of this study was to analyse the role of body composition parameters in severely injured trauma patients. METHODS All consecutive patients requiring emergency tracheal intubation and mechanical ventilation before initial computed tomography (CT) at a level-1 trauma centre over a 12-year period (2008-2019) were reanalysed. The analysis included CT-derived body composition parameters based upon whole-body trauma CT as prognostic variables for 30-day mortality, intensive care unit length of stay (ICU LOS) and mechanical ventilation duration. RESULTS Four hundred seventy-two patients (75% male) with a median age of 49 years, median injury severity score of 26 and 30-day mortality rate of 22% (104 patients) met the inclusion criteria and were analysed. Regarding body composition parameters, 231 patients (49%) had visceral obesity, 75 patients had sarcopenia (16%) and 35 patients had sarcopenic obesity (7.4%). After adjustment for statistically significant univariable predictors age, body mass index, sarcopenic obesity, visceral obesity, American Society of Anesthesiologists classification ≥3, injury severity score and Glasgow Coma Scale ≤ 8 points, the Cox proportional hazard model identified sarcopenia as significant prognostic factor of 30-day mortality (hazard ratio 2.84; 95% confidence interval 1.38-5.85; P = 0.004), which was confirmed in Kaplan-Meier survival analysis (log-rank P = 0.006). In a subanalysis of 363 survivors, linear multivariable regression analysis revealed no significant associations of body composition parameters with ICU LOS and duration of mechanical ventilation. CONCLUSIONS In a multivariable analysis of mechanically ventilated trauma patients, CT-defined sarcopenia was significantly associated with 30-day mortality whereas no associations of body composition parameters with ICU LOS and duration of mechanical ventilation were observed.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, Leipzig, Germany
| | - Tihomir Dermendzhiev
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, Leipzig, Germany
| | - Michael Hetz
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Georg Osterhoff
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Christian Kleber
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, Leipzig, Germany
| | - Jeanette Henkelmann
- Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, Leipzig, Germany
| | - Robert Werdehausen
- Department of Anesthesiology and Intensive Care, Medical Faculty, University of Magdeburg, Magdeburg, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Gunther Hempel
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Manuel F Struck
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
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Tsukagoshi M, Araki K, Shirabe K. Pancreatic cancer and sarcopenia: a narrative review of the current status. Int J Clin Oncol 2024; 29:1055-1066. [PMID: 38954075 DOI: 10.1007/s10147-024-02576-2] [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: 05/25/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Pancreatic cancer is still a difficult disease to treat, despite recent advances in surgical techniques and chemotherapeutic drugs. Its incidence continues to rise, as does the number of older patients. Sarcopenia is defined as a progressive and generalized loss of skeletal muscle mass and strength. Sarcopenia is present in approximately 40% in patients with pancreatic cancer. Sarcopenia is primarily diagnosed through imaging, and progress is being made in the development of automated methods and artificial intelligence, as well as biomarker research. Sarcopenia has been linked to a poor prognosis in pancreatic cancer patients. However, some studies suggest that sarcopenia is not always associated with a poor prognosis, depending on the resectability of pancreatic cancer and the nature of treatment, such as surgery or chemotherapy. Recent meta-analyses have found that sarcopenia is not linked to postoperative complications. It is still debated whether there is a link between sarcopenia and drug toxicity during chemotherapy. The relationship between sarcopenia and immunity has been investigated, but the mechanism is still unknown.
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Affiliation(s)
- Mariko Tsukagoshi
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgical Science, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi Gunma, 371-8511, Japan
| | - Kenichiro Araki
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgical Science, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi Gunma, 371-8511, Japan
| | - Ken Shirabe
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgical Science, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi Gunma, 371-8511, Japan.
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Sales-Balaguer N, Sorribes-Carreras P, Morillo Macias V. Diagnosis of Sarcopenia and Myosteatosis by Computed Tomography in Patients with Esophagogastric and Pancreatic Cancer. Cancers (Basel) 2024; 16:2738. [PMID: 39123465 PMCID: PMC11311307 DOI: 10.3390/cancers16152738] [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: 07/03/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
The increase in the global incidence of cancer highlights the need to continue advancing in the techniques of diagnosis and nutritional assessment of cancer patients, given the prognostic and therapeutic impact of nutritional status. In this study, sarcopenia was evaluated as an independent predictor of morbidity and mortality. Data from 45 patients diagnosed with esophagogastric or pancreatic cancer were analyzed. Body composition was determined using computed tomography images, and functionality tests were performed. Sarcopenia was present in 22.2% of the patients, while only 31.1% had correct musculature. A reduction in muscle mass or function was observed in 46.7% of the patients. Likewise, the prevalence of myosteatosis reached 60% of the patients. No significant differences were found with regard to the presence of sarcopenia according to BMI classifications, so it is necessary to evaluate the patient with body composition techniques that include the evaluation of the different muscle and fat compartments. In conclusion, a comprehensive intervention is necessary to improve the detection of sarcopenia/myosteatosis and, in the future, to be able to carry out an approach that improves the quality of life and survival rates of patients.
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Affiliation(s)
- Nerea Sales-Balaguer
- Castelló Provincial Hospital Consortium Foundation, 12002 Castelló de la Plana, Spain
| | - Patricia Sorribes-Carreras
- Nutrition and Dietetics Unit, Provincial Hospital Consortium of Castelló (CHPC), 12002 Castelló de la Plana, Spain;
| | - Virginia Morillo Macias
- Radiotherapy Service, Provincial Hospital Consortium of Castelló (CHPC), 12002 Castelló de la Plana, Spain
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Hiraoka A, Kumada T, Tada T, Toyoda H, Kariyama K, Hatanaka T, Kakizaki S, Naganuma A, Itobayashi E, Tsuji K, Ishikawa T, Ohama H, Tada F, Nouso K. Attempt to Establish Prognostic Predictive System for Hepatocellular Carcinoma Using Artificial Intelligence for Assistance with Selection of Treatment Modality. Liver Cancer 2023; 12:565-575. [PMID: 38058420 PMCID: PMC10697750 DOI: 10.1159/000530078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/06/2023] [Indexed: 12/08/2023] Open
Abstract
Introduction Because of recent developments in treatments for hepatocellular carcinoma (HCC), methods for determining suitable therapy for initial or recurrent HCC have become important. This study used artificial intelligence (AI) findings to establish a system for predicting prognosis of HCC patients at time of reoccurrence based on clinical data as a reference for selection of treatment modalities. Methods As a training cohort, 5,701 observations obtained at the initial and each subsequent treatment for recurrence from 1,985 HCC patients at a single center from 2000 to 2021 were used. The validation cohort included 5,692 observations from patients at multiple centers obtained at the time of the initial treatment. An AI calculating system (PRAID) was constructed based on 25 clinical factors noted at each treatment from the training cohort, and then predictive prognostic values for 1- and 3-year survival in both cohorts were evaluated. Results After exclusion of patients lacking clinical data regarding albumin-bilirubin (ALBI) grade or tumor-node-metastasis stage of the Liver Cancer Study Group of Japan, 6th edition (TNM-LCSGJ 6th), ALBI-TNM-LCSGJ 6th (ALBI-T) and modified ALBI-T scores confirmed that prognosis for patients in both cohorts was similar. The area under the curve for prediction of both 1- and 3-year survival in the validation cohort was 0.841 (sensitivity 0.933 [95% CI: 0.925-0.940], specificity 0.517 [95% CI: 0.484-0.549]) and 0.796 (sensitivity 0.806 [95% CI: 0.790-0.821], specificity 0.646 [95% CI: 0.624-0.668]), respectively. Conclusion The present PRAID system might provide useful prognostic information related to short and medium survival for decision-making regarding the best therapeutic modality for both initial and recurrent HCC cases.
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Affiliation(s)
- Atsushi Hiraoka
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Takashi Kumada
- Department of Nursing, Gifu Kyoritsu University, Ogaki, Japan
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Toshifumi Tada
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Hidenori Toyoda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Kazuya Kariyama
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
| | - Takeshi Hatanaka
- Department of Gastroenterology, Gunma Saiseikai Maebashi Hospital, Maebashi, Japan
| | - Satoru Kakizaki
- Department of Clinical Research, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
| | - Atsushi Naganuma
- Department of Gastroenterology, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
| | - Ei Itobayashi
- Department of Gastroenterology, Asahi General Hospital, Asahi, Japan
| | - Kunihiko Tsuji
- Center of Gastroenterology, Teine Keijinkai Hospital, Sapporo, Japan
| | - Toru Ishikawa
- Department of Gastroenterology, Saiseikai Niigata Hospital, Niigata, Japan
| | - Hideko Ohama
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Fujimasa Tada
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Kazuhiro Nouso
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
| | - on behalf of the Real-life Practice Experts for HCC (RELPEC) Study Group
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
- Department of Nursing, Gifu Kyoritsu University, Ogaki, Japan
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
- Department of Gastroenterology, Gunma Saiseikai Maebashi Hospital, Maebashi, Japan
- Department of Clinical Research, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
- Department of Gastroenterology, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
- Department of Gastroenterology, Asahi General Hospital, Asahi, Japan
- Center of Gastroenterology, Teine Keijinkai Hospital, Sapporo, Japan
- Department of Gastroenterology, Saiseikai Niigata Hospital, Niigata, Japan
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Tram NK, Chou TH, Janse SA, Bobbey AJ, Audino AN, Onofrey JA, Stacy MR. Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects. Eur Radiol 2023; 33:6599-6607. [PMID: 36988714 DOI: 10.1007/s00330-023-09587-z] [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: 08/26/2022] [Revised: 02/07/2023] [Accepted: 02/17/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVES The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, and young adult (AYA) patients with lymphoma. METHODS This 10-year retrospective, single-site study of 110 pediatric and AYA patients with lymphoma involved manual segmentation of fat and muscle tissue from 260 CT imaging datasets obtained as part of routine imaging at initial staging and first therapeutic follow-up. A DL model was trained to perform semiautomated image segmentation of adipose and muscle tissue. The association between BC measures and the occurrence of 3-year late effects was evaluated using Cox proportional hazards regression analyses. RESULTS DL-guided measures of BC were in close agreement with those obtained by a human rater, as demonstrated by high Dice scores (≥ 0.95) and correlations (r > 0.99) for each tissue of interest. Cox proportional hazards regression analyses revealed that patients with elevated subcutaneous adipose tissue at baseline and first follow-up, along with patients who possessed lower volumes of skeletal muscle at first follow-up, have increased risk of late effects compared to their peers. CONCLUSIONS DL provides rapid and accurate quantification of image-derived measures of BC that are associated with risk for treatment-related late effects in pediatric and AYA patients with lymphoma. Image-based monitoring of BC measures may enhance future opportunities for personalized medicine for children with lymphoma by identifying patients at the highest risk for late effects of treatment. KEY POINTS • Deep learning-guided CT image analysis of body composition measures achieved high agreement level with manual image analysis. • Pediatric patients with more fat and less muscle during the course of cancer treatment were more likely to experience a serious adverse event compared to their clinical counterparts. • Deep learning of body composition may add value to routine CT imaging by offering real-time monitoring of pediatric, adolescent, and young adults at high risk for late effects of cancer treatment.
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Affiliation(s)
- Nguyen K Tram
- Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA
| | - Ting-Heng Chou
- Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA
| | - Sarah A Janse
- Center for Biostatistics, The Ohio State University, Columbus, OH, USA
| | - Adam J Bobbey
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH, USA
| | - Anthony N Audino
- Division of Hematology/Oncology/BMT, Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - John A Onofrey
- Department of Radiology & Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Urology, Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Mitchel R Stacy
- Center for Regenerative Medicine, The Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, WB4133, Columbus, OH, 43215, USA.
- Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA.
- Division of Vascular Diseases and Surgery, Department of Surgery, The Ohio State University College of Medicine, Columbus, OH, USA.
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Ali S, Lee YR, Park SY, Tak WY, Jung SK. Abdominal CT Segmentation for Body Composition Assessment Using Network Consistency Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082821 DOI: 10.1109/embc40787.2023.10340476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Estimating skeletal muscle (SM) and adipose tissues is an invaluable prognostic indicator in cancer treatment, major surgeries, and general health screening. Body composition is usually measured with abdominal computed tomography (CT) scans acquired in clinical settings. The whole-body SM volume is correlated with the estimated SM based on the measurement of a single two-dimensional vertebral slice. It is necessary to label a CT image at the pixel level to estimate SM, known as semantic segmentation. In this work, we trained a segmentation model using the labeled abdominal CT slices and the additional unlabeled slices. In particular, we trained two identical segmentation networks with differently initialized weights. Network Consistency Learning (NCL) allowed learning from unlabeled images by forcing the predictions from both networks to be the same. We segmented abdominal CT images from a newly created in-house dataset. The proposed approach gained 10% better performance in terms of Dice similarity score (DSC) than that obtained by a standard supervised network demonstrating the effectiveness of NCL in exploiting unlabeled images.Clinical relevance- An efficient and cost-effective method is proposed for assessing body composition from limited labeled and abundant unlabeled CT images to facilitate fast diagnosis, prognosis, and interventions.
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Yang L, Liao X, Xie Z, Li H. Prognostic value of pretreatment skeletal muscle index in pancreatic carcinoma patients: A meta-analysis. Medicine (Baltimore) 2023; 102:e33663. [PMID: 37171343 PMCID: PMC10174348 DOI: 10.1097/md.0000000000033663] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND The association between pretreatment skeletal muscle index (SMI) and long-term survival of pancreatic carcinoma patients remains unclear up to now. METHODS The PubMed, Web of Science and EMBASE databases were searched up to March 1, 2022 for relevant studies. The primary and secondary outcomes were overall survival and progression-free survival, respectively. The hazard ratios (HRs) and 95% confidence intervals (CIs) were combined to assess the relationship between pretreatment SMI and prognosis of pancreatic carcinoma patients. All statistical analysis was conducted by STATA 15.0 software. RESULTS Twenty retrospective studies involving 3765 patients were included. The pooled results demonstrated that lower pretreatment SMI was significantly related to poorer overall survival (HR = 1.42, 95% CI: 1.25-1.62, P < .001) and progression-free survival (HR = 1.41, 95% CI: 1.08-1.84, P = .012). Besides subgroup analysis based on the treatment (non-surgery vs surgery) and tumor stage (advanced vs early stage) showed similar results. CONCLUSION Pretreatment SMI could serve as a promising and reliable prognostic factor for pancreatic carcinoma patients and lower pretreatment SMI predicted worse prognosis.
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Affiliation(s)
- Li Yang
- Department of Digestive Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, P.R. China
| | - Xianghui Liao
- Department of Digestive Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, P.R. China
| | - Zhong Xie
- Department of Digestive Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, P.R. China
| | - Haiwen Li
- Department of Head and Neck Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, P.R. China
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [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: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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Sim JH, Kim KW, Ko Y, Kwon HM, Moon YJ, Jun IG, Kim SH, Kim S, Song JG, Hwang GS. Association of sex-specific donor skeletal muscle index with surgical outcomes in living donor liver transplantation recipients. Liver Int 2023; 43:684-694. [PMID: 36377561 DOI: 10.1111/liv.15478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/28/2022] [Accepted: 11/13/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND A recent study reported a correlation between the muscle mass of male donors and graft failure in living donor liver transplantation (LDLT) recipients. We investigated the association of sex-specific donor skeletal muscle index (SMI) with mortality and graft failure in LDLT recipients. METHODS We retrospectively analysed 2750 sets of donors and recipients between January 2008 and January 2018. The recipient outcomes were analysed by dividing the data according to donor sex. Cox regression analyses were performed to evaluate the association between donor SMI by sex and 1-year mortality and graft failure in recipients. RESULTS In the male donor group, robust donor (increased SMI) was significantly associated with higher risks for mortality (hazard ratio [HR]: 1.03, 95% confidence interval [CI]: 1.00-1.06, p = .023) and graft failure (HR: 1.04, 95% CI: 1.01-1.06, p = .007) at 1 year. In the female donor group, the robust donor was significantly associated with lower risks for mortality (HR: 0.92, 95% CI: 0.87-0.97, p = .003) and graft failure (HR: 0.95, 95% CI: 0.90-1.00, p = .032) at 1 year. CONCLUSIONS Donor SMI was associated with surgical outcomes in recipients. Robust male and female donors were a significant negative and protective factor for grafts respectively.
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Affiliation(s)
- Ji-Hoon Sim
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyung-Won Kim
- Department of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - YouSun Ko
- Department of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hye-Mee Kwon
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young-Jin Moon
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - In-Gu Jun
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sung-Hoon Kim
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jun-Gol Song
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyu-Sam Hwang
- Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Couderc AL, Liuu E, Boudou-Rouquette P, Poisson J, Frelaut M, Montégut C, Mebarki S, Geiss R, ap Thomas Z, Noret A, Pierro M, Baldini C, Paillaud E, Pamoukdjian F. Pre-Therapeutic Sarcopenia among Cancer Patients: An Up-to-Date Meta-Analysis of Prevalence and Predictive Value during Cancer Treatment. Nutrients 2023; 15:nu15051193. [PMID: 36904192 PMCID: PMC10005339 DOI: 10.3390/nu15051193] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
This study will address the prevalence of pre-therapeutic sarcopenia (PS) and its clinical impact during cancer treatment among adult cancer patients ≥ 18 years of age. A meta-analysis (MA) with random-effect models was performed via a MEDLINE systematic review, according to the PRISMA statement, focusing on articles published before February 2022 that reported observational studies and clinical trials on the prevalence of PS and the following outcomes: overall survival (OS), progression-free survival (PFS), post-operative complications (POC), toxicities (TOX), and nosocomial infections (NI). A total of 65,936 patients (mean age: 45.7-85 y) with various cancer sites and extensions and various treatment modes were included. Mainly defined by CT scan-based loss of muscle mass only, the pooled prevalence of PS was 38.0%. The pooled relative risks were 1.97, 1.76, 2.70, 1.47, and 1.76 for OS, PFS, POC, TOX, and NI, respectively (moderate-to-high heterogeneity, I2: 58-85%). Consensus-based algorithm definitions of sarcopenia, integrating low muscle mass and low levels of muscular strength and/or physical performance, lowered the prevalence (22%) and heterogeneity (I2 < 50%). They also increased the predictive values with RRs ranging from 2.31 (OS) to 3.52 (POC). PS among cancer patients is prevalent and strongly associated with poor outcomes during cancer treatment, especially when considering a consensus-based algorithm approach.
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Affiliation(s)
- Anne-Laure Couderc
- Internal Medicine Geriatrics and Therapeutic Unit, APHM, 13009 Marseille, France
- CNRS, EFS, ADES, Aix-Marseille University, 13015 Marseille, France
| | - Evelyne Liuu
- Department of Geriatrics, CHU Poitiers, 86000 Poitiers, France
- CIC1402 INSERM Unit, Poitiers University Hospital, 86000 Poitiers, France
| | - Pascaline Boudou-Rouquette
- Ariane Program, Department of Medical Oncology, Cochin Hospital, Paris Cancer Institute CARPEM, APHP, 75014 Paris, France
- INSERM U1016-CNRS UMR8104, Cochin Institute, Paris Cancer Institute CARPEM, Paris Cité University, 75015 Paris, France
| | - Johanne Poisson
- Department of Geriatrics, Georges Pompidou European Hospital, Paris Cancer Institute CARPEM, APHP, 75015 Paris, France
- Faculty of Health, Paris Cité University, 75006 Paris, France
| | - Maxime Frelaut
- Department of Medical Oncology, Gustave Roussy Institute, 94805 Villejuif, France
| | - Coline Montégut
- Internal Medicine Geriatrics and Therapeutic Unit, APHM, 13009 Marseille, France
- Coordination Unit for Geriatric Oncology (UCOG), PACA West, 13009 Marseille, France
| | - Soraya Mebarki
- Department of Geriatrics, Georges Pompidou European Hospital, Paris Cancer Institute CARPEM, APHP, 75015 Paris, France
| | - Romain Geiss
- Department of Medical Oncology, Curie Institute, 92210 Saint-Cloud, France
| | - Zoé ap Thomas
- Department of Cancer Medicine, Gustave Roussy Institute, 94805 Villejuif, France
| | - Aurélien Noret
- Department of Geriatrics, Georges Pompidou European Hospital, Paris Cancer Institute CARPEM, APHP, 75015 Paris, France
| | - Monica Pierro
- Department of Geriatrics, Georges Pompidou European Hospital, Paris Cancer Institute CARPEM, APHP, 75015 Paris, France
| | - Capucine Baldini
- Drug Development Department, Gustave Roussy Institute, 94805 Villejuif, France
| | - Elena Paillaud
- Department of Geriatrics, Georges Pompidou European Hospital, Paris Cancer Institute CARPEM, APHP, 75015 Paris, France
- INSERM, IMRB, Clinical, Epidemiology and Ageing, Université Paris-Est Creteil, 94010 Creteil, France
| | - Frédéric Pamoukdjian
- Department of Geriatrics, Avicenne Hospital, APHP, 93000 Bobigny, France
- INSERM UMR_S942 Cardiovascular Markers in Stressed Conditions MASCOT, Sorbonne Paris Nord University, 93000 Bobigny, France
- Correspondence:
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Affiliation(s)
- Theresa C McLoud
- From the Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, MZ-FND 216, Boston, MA 02114-2696 (T.C.M.); and Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Brent P Little
- From the Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, MZ-FND 216, Boston, MA 02114-2696 (T.C.M.); and Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
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12
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Choi MH, Yoon SB. Sarcopenia in pancreatic cancer: Effect on patient outcomes. World J Gastrointest Oncol 2022; 14:2302-2312. [PMID: 36568942 PMCID: PMC9782618 DOI: 10.4251/wjgo.v14.i12.2302] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/29/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Pancreatic cancer is a challenging disease with an increasing incidence and extremely poor prognosis. The clinical outcomes of pancreatic cancer depend on tumor biology, responses to treatments, and malnutrition or cachexia. Sarcopenia represents a severe catabolic condition defined by the age-related loss of muscle mass and strength and affects as much as 70% of malnourished pancreatic cancer patients. The lumbar skeletal muscle index, defined as the total abdominal muscle area at the L3 vertebral level adjusted by the square of the height, is widely used for assessing sarcopenia in patients with pancreatic cancer. Several studies have suggested that sarcopenia may be a risk factor for perioperative complications and decreased recurrence-free or overall survival in patients with pancreatic cancer undergoing surgery. Sarcopenia could also intensify chemotherapy-induced toxicities and worsen the quality of life and survival in the neoadjuvant or palliative chemotherapy setting. Sarcopenia, not only at the time of diagnosis but also during treatment, decreases survival in patients with pancreatic cancer. Theoretically, multimodal interventions may improve sarcopenia and clinical outcomes; however, no study has reported positive results. Further prospective studies are needed to confirm the prognostic role of sarcopenia and the effects of multimodal interventions in patients with pancreatic cancer.
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Affiliation(s)
- Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, South Korea
| | - Seung Bae Yoon
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 03312, South Korea
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13
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Cellina M, Cè M, Khenkina N, Sinichich P, Cervelli M, Poggi V, Boemi S, Ierardi AM, Carrafiello G. Artificial Intellgence in the Era of Precision Oncological Imaging. Technol Cancer Res Treat 2022; 21:15330338221141793. [PMID: 36426565 PMCID: PMC9703524 DOI: 10.1177/15330338221141793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, Milano, Italy,Michaela Cellina, MD, Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milano, Italy.
| | - Maurizio Cè
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Natallia Khenkina
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Polina Sinichich
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Marco Cervelli
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Vittoria Poggi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | - Sara Boemi
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
| | | | - Gianpaolo Carrafiello
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy,Radiology Department, Fondazione IRCCS Cà Granda, Milan, Italy
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14
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Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022; 95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.
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Affiliation(s)
- Luis Marti-Bonmati
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Department of Radiology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Leonor Cerdá-Alberich
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
| | | | | | - Eva Montalvá Orón
- Department of Surgery, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Judith Pérez Rojas
- Department of Pathology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Angel Alberich-Bayarri
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, Quibim
SL, Valencia,
Spain
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15
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Alavi DH, Sakinis T, Henriksen HB, Beichmann B, Fløtten A, Blomhoff R, Lauritzen PM. Body composition assessment by artificial intelligence from routine computed tomography scans in colorectal cancer: Introducing BodySegAI. JCSM CLINICAL REPORTS 2022. [DOI: 10.1002/crt2.53] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Dena Helene Alavi
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Tomas Sakinis
- Medical Division, Radiology and Nuclear Medicine, Neuroimaging Research Group Oslo University Hospital Oslo Norway
| | - Hege Berg Henriksen
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Benedicte Beichmann
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
| | - Ann‐Monica Fløtten
- Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway
| | - Rune Blomhoff
- Department of Nutrition, Institute of Basic Medical Sciences University of Oslo Oslo Norway
- Department of Clinical Service, Division of Cancer Medicine Oslo University Hospital Oslo Norway
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16
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Influence of Baseline CT Body Composition Parameters on Survival in Patients with Pancreatic Adenocarcinoma. J Clin Med 2022; 11:jcm11092356. [PMID: 35566483 PMCID: PMC9105849 DOI: 10.3390/jcm11092356] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer is the seventh leading cause of cancer death in both sexes. The aim of this study is to analyze baseline CT body composition using artificial intelligence to identify possible imaging predictors of survival. We retrospectively included 103 patients. First, the presence of surgical treatment and cut-off values for sarcopenia and obesity served as independent variates. Second, the presence of surgery, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) served as independent variates. Cox regression analysis was performed for 1-year, 2-year, and 3-year survival. Possible differences between patients undergoing surgical versus nonsurgical treatment were analyzed. Presence of surgery significantly predicted 1-year, 2-year, and 3-year survival (p = 0.01, <0.001, and <0.001, respectively). Across the follow-up periods of 1-year, 2-year, and 3-year survival, the presence of sarcopenia became an equally important predictor of survival (p = 0.25, 0.07, and <0.001, respectively). Additionally, increased VAT predicted 2-year and 3-year survival (p = 0.02 and 0.04, respectively). The impact of sarcopenia on 3-year survival was higher in the surgical treatment group (p = 0.02 and odds ratio = 2.57) compared with the nonsurgical treatment group (p = 0.04 and odds ratio = 1.92). Fittingly, a lower SMI significantly affected 3-year survival only in patients who underwent surgery (p = 0.02). Especially if surgery is performed, AI-derived sarcopenia and reduced muscle mass are unfavorable imaging predictors.
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17
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Schiaffino S. Editorial comment to artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis. Eur J Radiol 2022; 151:110292. [PMID: 35397407 DOI: 10.1016/j.ejrad.2022.110292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 03/31/2022] [Indexed: 11/03/2022]
Affiliation(s)
- Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy.
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18
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The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer. DISEASE MARKERS 2022; 2022:1819841. [PMID: 35392497 PMCID: PMC8983171 DOI: 10.1155/2022/1819841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 02/15/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022]
Abstract
Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.
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Capurso G, Pecorelli N, Burini A, Orsi G, Palumbo D, Macchini M, Mele R, de Cobelli F, Falconi M, Arcidiacono PG, Reni M. The impact of nutritional status on pancreatic cancer therapy. Expert Rev Anticancer Ther 2022; 22:155-167. [PMID: 34989653 DOI: 10.1080/14737140.2022.2026771] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/05/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive disease with poor outcomes. One of the reasons for the dismal prognosis resides in its impressive ability to alter the nutritional status of patients who develop malnutrition, cachexia, anorexia, and sarcopenia in most cases. The ideal way to measure such changes in PDAC patients, in order to readily identify them and avoid complications or discontinuations of treatment is a relatively unexplored area. In addition, most PDAC patients experience pancreatic exocrine insufficiency (PEI) that contributes to the complex puzzle of malnutrition and that can be treated with Pancreatic Enzyme Replacement Therapy (PERT). AREAS COVERED We review current knowledge on the impact of nutritional status on both surgical and medical treatments for PDAC, reporting available data on the causes of malnutrition, characteristics, and advantages of different tools to investigate nutritional status and possible strategies to improve patient outcomes. EXPERT OPINION All PDAC patients should receive a careful nutritional assessment at diagnosis, and this should be repeated alongside their treatment path. Screening tools and biochemical variables or scores are associated with prognosis, but bioimpedance vector analysis (BIVA) and radiological assessment of body composition seem more accurate in predicting clinical outcomes and postoperative complications.
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Affiliation(s)
- Gabriele Capurso
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Nicolò Pecorelli
- Pancreas Translational & Clinical Research Center, Division of Pancreatic Surgery, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Alice Burini
- Nutrition Service, San Raffaele Scientific Institute IRCCS, Milan, Italy
| | - Giulia Orsi
- Pancreas Translational & Clinical Research Center, Oncology Department, San Raffaele Scientific Institute IRCCS, Milan, Italy
| | - Diego Palumbo
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Marina Macchini
- Pancreas Translational & Clinical Research Center, Oncology Department, San Raffaele Scientific Institute IRCCS, Milan, Italy
| | - Roberto Mele
- Nutrition Service, San Raffaele Scientific Institute IRCCS, Milan, Italy
| | - Francesco de Cobelli
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Falconi
- Pancreas Translational & Clinical Research Center, Division of Pancreatic Surgery, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Paolo Giorgio Arcidiacono
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Michele Reni
- Pancreas Translational & Clinical Research Center, Oncology Department, San Raffaele Scientific Institute IRCCS, Milan, Italy
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20
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Chen X, Fu R, Shao Q, Chen Y, Ye Q, Li S, He X, Zhu J. Application of artificial intelligence to pancreatic adenocarcinoma. Front Oncol 2022; 12:960056. [PMID: 35936738 PMCID: PMC9353734 DOI: 10.3389/fonc.2022.960056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. METHODS We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. RESULTS Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. CONCLUSIONS AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.
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Affiliation(s)
- Xi Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ruibiao Fu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Shao
- Department of Surgical Ward 1, Ningbo Women and Children’s Hospital, Ningbo, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Jinhui Zhu,
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