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Wang T, Tan J, Wang T, Xiang S, Zhang Y, Jian C, Jian J, Zhao W. A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients. Pragmat Obs Res 2024; 15:121-137. [PMID: 39130528 PMCID: PMC11316486 DOI: 10.2147/por.s464439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes. Patients and Methods In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records. They were categorized into success (n=479) and failure (n=391) groups based on average blood pressure control efficacy. Predictive models were constructed using six machine learning algorithms. Evaluation metrics encompassed the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis assessed feature contributions to efficacy. Results All six machine learning models demonstrated superior predictive performance. Following variable reduction, the model predicting amlodipine efficacy was reconstructed using these algorithms, with the light gradient boosting machine (LightGBM) model achieving the highest overall performance (AUC = 0.803). Notably, amlodipine showed enhanced efficacy in patients with low platelet distribution width (PDW) values, as well as high hematocrit (HCT) and thrombin time (TT) values. Conclusion This study utilized machine learning to predict amlodipine's effectiveness in hypertension treatment, pinpointing key factors: HCT, PDW, and TT levels. Lower PDW, along with higher HCT and TT, correlated with enhanced treatment outcomes. This facilitates personalized treatment, particularly for hospitalized hypertensive patients undergoing amlodipine monotherapy.
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Affiliation(s)
- Tingting Wang
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Juntao Tan
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China
| | - Tiantian Wang
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China
| | - Shoushu Xiang
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China
| | - Yang Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Chang Jian
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Jie Jian
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Wenlong Zhao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, People’s Republic of China
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Tashiro H, Onoe T, Tanimine N, Tazuma S, Shibata Y, Sudo T, Sada H, Shimada N, Tazawa H, Suzuki T, Shimizu Y. Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer. J Hepatocell Carcinoma 2024; 11:1323-1330. [PMID: 38983935 PMCID: PMC11232954 DOI: 10.2147/jhc.s451025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/11/2024] [Indexed: 07/11/2024] Open
Abstract
Background Posthepatectomy liver failure (PHLF) is a serious complication associated with high mortality rates. Machine learning (ML) has rapidly developed and may outperform traditional models in predicting PHLF in patients who have undergone hepatectomy. This study aimed to predict PHLF using ML and compare its performance with that of traditional scoring systems. Methods The clinicopathological data of 334 patients who underwent liver resection were retrospectively collected. The Pycaret library, a simple, open-source machine learning library, was used to compare multiple classification models for PHLF prediction. The predictive performance of 15 ML algorithms was compared using the mean area under the receiver operating characteristic curve (AUROC) and accuracy, and the best-fit model was selected among 15 ML algorithms. Next, the predictive performance of the selected ML-PHLF model was compared with that of routine scoring systems, the albumin-bilirubin score (ALBI) and the fibrosis-4 (FIB-4) index, using AUROC. Results The best model was extreme gradient boosting (accuracy:93.1%; AUROC:0.863) among the 15 ML algorithms. As compared with ALBI and FIB-4, the ML PHLF model had higher AUROC for predicting PHLF. Conclusion The novel ML model for predicting PHLF outperformed routine scoring systems.
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Affiliation(s)
- Hirotaka Tashiro
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Takashi Onoe
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Naoki Tanimine
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Sho Tazuma
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Yoshiyuki Shibata
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Takeshi Sudo
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Haruki Sada
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Norimitsu Shimada
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Hirofumi Tazawa
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Takahisa Suzuki
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
| | - Yosuke Shimizu
- Department of Surgery, Kure Medical Center Chugoku Cancer Center, National Hospital Organization, Kure, Hiroshima, Japan
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Li Q, Fang J, Liu K, Luo P, Wang X. Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma. Front Immunol 2024; 15:1424806. [PMID: 38983852 PMCID: PMC11231095 DOI: 10.3389/fimmu.2024.1424806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/06/2024] [Indexed: 07/11/2024] Open
Abstract
Background The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells. Methods This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis. Results In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells. Conclusion The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.
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Affiliation(s)
- Qingbiao Li
- Department of Orthopedics, Southern Medical University Pingshan Hospital (Pingshan District Peoples’ Hospital of Shenzhen), Shenzhen, Guangdong, China
| | - Jiarui Fang
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Kai Liu
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Peng Luo
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Xiuzhuo Wang
- Department of Orthopedics, Southern Medical University Pingshan Hospital (Pingshan District Peoples’ Hospital of Shenzhen), Shenzhen, Guangdong, China
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4
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Yao S, Yao D, Huang Y, Qin S, Chen Q. A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification. Front Endocrinol (Lausanne) 2024; 15:1381822. [PMID: 38957447 PMCID: PMC11218542 DOI: 10.3389/fendo.2024.1381822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Objective This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. Methods 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. Results The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. Conclusion The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.
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Affiliation(s)
- Shunhan Yao
- Medical College, Guangxi University, Nanning, China
- Monash Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia
| | - Dunwei Yao
- Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Gastroenterology, The People’s Hospital of Baise, Baise, China
| | - Yuanxiang Huang
- School of Computer, Electronic and Information, Guangxi University, Nanning, China
| | - Shanyu Qin
- Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qingfeng Chen
- School of Computer, Electronic and Information, Guangxi University, Nanning, China
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5
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Wang Y, Li C, He J, Zhao Q, Zhou Y, Sun H, Zhu H, Ding B, Ren M. Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy. Front Immunol 2024; 15:1426474. [PMID: 38947325 PMCID: PMC11211272 DOI: 10.3389/fimmu.2024.1426474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 05/31/2024] [Indexed: 07/02/2024] Open
Abstract
Background Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.
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Affiliation(s)
- YaXuan Wang
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chao Li
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - JiaXing He
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - QingYun Zhao
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yu Zhou
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - HaoDong Sun
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - HaiXia Zhu
- Clinical Laboratory, Tumor Hospital Affiliated to Nantong University, Nantong, China
| | - BeiChen Ding
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - MingHua Ren
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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6
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Gairola S, Solanki SL, Patkar S, Goel M. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian J Surg Oncol 2024; 15:186-195. [PMID: 38818006 PMCID: PMC11133260 DOI: 10.1007/s13193-024-01883-4] [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: 09/18/2023] [Accepted: 01/16/2024] [Indexed: 06/01/2024] Open
Abstract
Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.
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Affiliation(s)
- Shruti Gairola
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Sohan Lal Solanki
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Shraddha Patkar
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Mahesh Goel
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
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Lopez-Lopez V, Morise Z, Albaladejo-González M, Gavara CG, Goh BKP, Koh YX, Paul SJ, Hilal MA, Mishima K, Krürger JAP, Herman P, Cerezuela A, Brusadin R, Kaizu T, Lujan J, Rotellar F, Monden K, Dalmau M, Gotohda N, Kudo M, Kanazawa A, Kato Y, Nitta H, Amano S, Valle RD, Giuffrida M, Ueno M, Otsuka Y, Asano D, Tanabe M, Itano O, Minagawa T, Eshmuminov D, Herrero I, Ramírez P, Ruipérez-Valiente JA, Robles-Campos R, Wakabayashi G. Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study. Surg Endosc 2024; 38:2411-2422. [PMID: 38315197 PMCID: PMC11078826 DOI: 10.1007/s00464-024-10681-6] [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: 08/07/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
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Affiliation(s)
- Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Zeniche Morise
- Department of Surgery, Fujita Health University School of Medicine Okazaki Medical Center, Okazaki, Aichi, Japan
| | | | - Concepción Gomez Gavara
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Brian K P Goh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sijberden Jasper Paul
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Mohammed Abu Hilal
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Department of Surgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kohei Mishima
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
| | - Jaime Arthur Pirola Krürger
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo Herman
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Alvaro Cerezuela
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Roberto Brusadin
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Takashi Kaizu
- Department of General, Pediatric and Hepatobiliary-Pancreatic Surgery, Kitasato University School of Medicine, Sagamihara, Japan
| | - Juan Lujan
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Kazuteru Monden
- Department of Surgery, Fukuyama City Hospital, Hiroshima, Japan
| | - Mar Dalmau
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Naoto Gotohda
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Kudo
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Akishige Kanazawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka City General Hospital, Osaka, Japan
| | - Yutaro Kato
- Department of Surgery, Fujita Health University, Toyoake, Japan
| | - Hiroyuki Nitta
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | - Satoshi Amano
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | | | - Mario Giuffrida
- General Surgery Unit, Parma University Hospital, Parma, Italy
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama City, Wakayama, Japan
| | | | - Daisuke Asano
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Minoru Tanabe
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Osamu Itano
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Takuya Minagawa
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Dilmurodjon Eshmuminov
- Department of Surgery and Transplantation, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irene Herrero
- Department of Surgery, Getafe University Hospital, Madrid, Spain
| | - Pablo Ramírez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | | | - Ricardo Robles-Campos
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Go Wakabayashi
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
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Grignaffini F, Barbuto F, Troiano M, Piazzo L, Simeoni P, Mangini F, De Stefanis C, Onetti Muda A, Frezza F, Alisi A. The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review. Diagnostics (Basel) 2024; 14:388. [PMID: 38396427 PMCID: PMC10887838 DOI: 10.3390/diagnostics14040388] [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: 12/27/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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Affiliation(s)
- Flavia Grignaffini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Francesco Barbuto
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Maurizio Troiano
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Patrizio Simeoni
- National Transport Authority (NTA), D02 WT20 Dublin, Ireland;
- Faculty of Lifelong Learning, South East Technological University (SETU), R93 V960 Carlow, Ireland
| | - Fabio Mangini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Cristiano De Stefanis
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | | | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Anna Alisi
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
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Kang CM, Ku HJ, Moon HH, Kim SE, Jo JH, Choi YI, Shin DH. Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence. J Clin Med 2024; 13:381. [PMID: 38256518 PMCID: PMC10816299 DOI: 10.3390/jcm13020381] [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: 12/06/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.
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Affiliation(s)
- Chol Min Kang
- Department of Applied Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21287, USA;
| | - Hyung June Ku
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
| | - Hyung Hwan Moon
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea;
| | - Ji Hoon Jo
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Young Il Choi
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Dong Hoon Shin
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
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