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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Saraiva MM, Ribeiro T, González-Haba M, Agudo Castillo B, Ferreira JPS, Vilas Boas F, Afonso J, Mendes F, Martins M, Cardoso P, Pereira P, Macedo G. Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study. Cancers (Basel) 2023; 15:4827. [PMID: 37835521 PMCID: PMC10571941 DOI: 10.3390/cancers15194827] [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: 08/03/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
| | - Mariano González-Haba
- Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, C/Joaquín Rodrigo, 28220 Majadahonda, Madrid, Spain
| | - Belén Agudo Castillo
- Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, C/Joaquín Rodrigo, 28220 Majadahonda, Madrid, Spain
| | - João P. S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.º 455/461, 4200-135 Porto, Portugal
| | - Filipe Vilas Boas
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
| | - Pedro Pereira
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200-319 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Alaimo L, Lima HA, Moazzam Z, Endo Y, Yang J, Ruzzenente A, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Cauchy F, Koerkamp BG, Endo I, Kitago M, Pawlik TM. Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:5406-5415. [PMID: 37210452 DOI: 10.1245/s10434-023-13636-8] [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: 02/17/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND The high incidence of early recurrence after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a detrimental effect on overall survival (OS). Machine-learning models may improve the accuracy of outcome prediction for malignancies. METHODS Patients who underwent curative-intent hepatectomy for ICC were identified using an international database. Three machine-learning models were trained to predict early recurrence (< 12 months after hepatectomy) using 14 clinicopathologic characteristics. The area under the receiver operating curve (AUC) was used to assess their discrimination ability. RESULTS In this study, 536 patients were randomly assigned to training (n = 376, 70.1%) and testing (n = 160, 29.9%) cohorts. Overall, 270 (50.4%) patients experienced early recurrence (training: n = 150 [50.3%] vs testing: n = 81 [50.6%]), with a median tumor burden score (TBS) of 5.6 (training: 5.8 [interquartile range {IQR}, 4.1-8.1] vs testing: 5.5 [IQR, 3.7-7.9]) and metastatic/undetermined nodes (N1/NX) in the majority of the patients (training: n = 282 [75.0%] vs testing n = 118 [73.8%]). Among the three different machine-learning algorithms, random forest (RF) demonstrated the highest discrimination in the training/testing cohorts (RF [AUC, 0.904/0.779] vs support vector machine [AUC, 0.671/0.746] vs logistic regression [AUC, 0.668/0.745]). The five most influential variables in the final model were TBS, perineural invasion, microvascular invasion, CA 19-9 lower than 200 U/mL, and N1/NX disease. The RF model successfully stratified OS relative to the risk of early recurrence. CONCLUSIONS Machine-learning prediction of early recurrence after ICC resection may inform tailored counseling, treatment, and recommendations. An easy-to-use calculator based on the RF model was developed and made available online.
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Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Henrique A Lima
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Zorays Moazzam
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Yutaka Endo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Jason Yang
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | | | | | | | - Matthew Weiss
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Todd W Bauer
- Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | | | - Carlo Pulitano
- Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France
| | - Bas Groot Koerkamp
- Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
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Alaimo L, Pawlik TM. ASO Author Reflections: Use of Machine Learning to Predict Early Recurrence After Resection of Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:5416-5417. [PMID: 37222944 DOI: 10.1245/s10434-023-13672-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 05/10/2023] [Indexed: 05/25/2023]
Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
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Choi WJ, Walker R, Rajendran L, Jones O, Gravely A, Englesakis M, Gallinger S, Hirschfield G, Hansen B, Sapisochin G. Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study. ANNALS OF SURGERY OPEN 2023; 4:e328. [PMID: 37746604 PMCID: PMC10513309 DOI: 10.1097/as9.0000000000000328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To conduct a systematic review, critical appraisal, and external validation of survival prediction tools for patients undergoing intrahepatic cholangiocarcinoma (iCCA) resection. Summary background data Despite the development of several survival prediction tools in recent years for patients undergoing iCCA resections, there is a lack of critical appraisal and external validation of these models. Methods We conducted a systematic review and critical appraisal of survival and recurrence prediction models for patients undergoing curative-intent iCCA resections. Studies were evaluated based on their model design, risk of bias, reporting, performance, and validation results. We identified the best model and externally validated it using our institution's data. Results This review included a total of 31 studies, consisting of 26 studies with original prediction tools and 5 studies that only conducted external validations. Among the 26, 54% of the studies conducted internal validations, 46% conducted external validations, and only 1 study scored a low risk of bias. Harrell's C-statistics ranged from 0.67 to 0.76 for internal validation and from 0.64 to 0.75 for external validation. Only 81% of the studies reported model calibration. Our external validation of the best model (Intrahepatic Cholangiocarcinoma [ICC]-Metroticket) estimated Harrell's and Uno's C-statistics of 0.67 (95% CI: 0.56-0.77) and Uno's time-dependent area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.53-0.88), with a Brier score of 0.20 (95% CI: 0.15-0.26) and good calibration plots. Conclusions Many prediction models have been published in recent years, but their quality remains poor, and minimal methodological quality improvement has been observed. The ICC-Metroticket was selected as the best model (Uno's time-dependent AUC of 0.71) for 5-year overall survival prediction in patients undergoing curative-intent iCCA resection.
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Affiliation(s)
- Woo Jin Choi
- From the Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Richard Walker
- From the Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Luckshi Rajendran
- From the Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Owen Jones
- University Health Network, HPB Surgical Oncology, Toronto, Ontario, Canada
| | - Annie Gravely
- University Health Network, HPB Surgical Oncology, Toronto, Ontario, Canada
| | - Marina Englesakis
- Library and Information Services, University Health Network, Toronto, Canada
| | - Steven Gallinger
- From the Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- University Health Network, HPB Surgical Oncology, Toronto, Ontario, Canada
| | - Gideon Hirschfield
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, Canada
| | - Bettina Hansen
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, Canada
- Department of Epidemiology & Biostatistics, Erasmus MC, Rotterdam, the Netherlands
| | - Gonzalo Sapisochin
- From the Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- University Health Network, HPB Surgical Oncology, Toronto, Ontario, Canada
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Zhang K, Ye B, Wu L, Ni S, Li Y, Wang Q, Zhang P, Wang D. Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma. Sci Rep 2023; 13:13532. [PMID: 37598277 PMCID: PMC10439907 DOI: 10.1038/s41598-023-40780-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 08/16/2023] [Indexed: 08/21/2023] Open
Abstract
The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.
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Affiliation(s)
- Kaijiong Zhang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Ye
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lichun Wu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Sujiao Ni
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qifeng Wang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Peng Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Dongsheng Wang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Huang T, Kong J, Liu H, Lin Z, Lin Q, Lou J, Zheng S, Bi X, Wang J, Guo W, Li F, Wang J, Zheng Y, Li J, Cheng S, Zhou W, Zeng Y. Dynamic evaluation of postoperative survival in intrahepatic cholangiocarcinoma patients who did not undergo lymphadenectomy: a multicenter study. Scand J Gastroenterol 2023; 58:178-184. [PMID: 36036215 DOI: 10.1080/00365521.2022.2113426] [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] [Indexed: 02/04/2023]
Abstract
BACKGROUND The prognosis of Intrahepatic cholangiocarcinoma (ICC) patients who did not undergo lymphadenectomy is difficult to assess. This study aims to have a dynamic evaluation on the postoperative survival of ICC patients by calculating conditional survival. METHODS Relevant data were from patients treated in 12 large-scale hospitals from December 2011 to December 2017. The influence of relevant clinical baseline data on the prognosis of ICC patients was analyzed by Cox regression. Conditional survival (CS) is a method that may predict the prognostic probability dynamically. For a patient with x years of survival, the 1-year CS (CS1) may be calculated as CS1= OS(x + 1)/OS(x). RESULT A total of 361 patients who met the criteria were included in the study. Conditional survival (CS) means that the patients' prognosis varies with survival time, meanwhile, relevant factors affecting the prognosis have a time-varying effect. The probability of survival assessed by CS1 increased year by year and the 1,2,3-year survival improved from 68.4% to 87.8%, while the postoperative actuarial OS decreased from 69.4% at 1 years to 36.9% at 3 years. CONCLUSIONS In terms of CS, the estimated survival for ICC varies with the increase of survival time after excision. Patients who live longer were likely to live longer. At the same time, with the passage of time, the role of the original adverse factors of the tumor would gradually decrease. Conditional survival allows a more accurate assessment of ICC patients who did not undergo lymphadenectomy.
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Affiliation(s)
- Tingfeng Huang
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Jie Kong
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Hongzhi Liu
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Zhipeng Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Qizhu Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Jianying Lou
- Department of Hepatobiliary Surgery, The Second Hospital Affiliated to Zhejiang University, Hangzhou, China
| | - Shuguo Zheng
- Department of Hepatobiliary Surgery, The Southwest Hospital Affiliated to the Army Medical University, Chongqing, China
| | - Xinyu Bi
- Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jianming Wang
- Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science Technology, Wuhan, China
| | - Wei Guo
- Department of Hepatobiliary Surgery, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China
| | - Fuyu Li
- Department of Hepatobiliary Surgery, The West China Hospital of Sichuan University, Chengdu, China
| | - Jian Wang
- Department of Hepatobiliary Surgery, Renji Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Yamin Zheng
- Department of Hepatobiliary Surgery, Xuanwu Hospital Affiliated to Capital Medical University, Beijing, China
| | - Jingdong Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chuanbei Medical University, Nanchong, China
| | - Shi Cheng
- Department of Hepatobiliary Surgery, Tiantan Hospital Affiliated to Capital Medical University, Beijing, China
| | - Weiping Zhou
- Department of Hepatobiliary Surgery III, Eastern Hepatobiliary Surgery Hospital, Secondary Military Medical University, Shanghai, China
| | - Yongyi Zeng
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
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Yu Z, Ye X, Liu H, Li H, Hao X, Zhang J, Kou F, Wang Z, Wei H, Gao F, Zhai Q. Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study. Front Oncol 2022; 12:893966. [PMID: 35719963 PMCID: PMC9203846 DOI: 10.3389/fonc.2022.893966] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022] Open
Abstract
Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked via importance score as follows: treatment protocols, weight, number of chemotherapy treatments, and number of metastases. Finally, the confusion matrix was used to validate the model for a dose regimen of 1,250 mg lapatinib (precision = 81% and recall = 95%), and for a dose regimen of 1,000 mg lapatinib (precision = 87% and recall = 64%). To conclude, we established a deep learning model to predict lapatinib dose based on important influencing variables selected from real-world evidence, to achieve an optimal individualized dose regimen with good predictive performance.
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Affiliation(s)
- Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xuan Ye
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Hongyue Liu
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Huan Li
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd., Dalian, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Fang Kou
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zeyuan Wang
- Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Hai Wei
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd., Beijing, China
| | - Qing Zhai
- Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
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Haghbin H, Aziz M. Artificial intelligence and cholangiocarcinoma: Updates and prospects. World J Clin Oncol 2022; 13:125-134. [PMID: 35316928 PMCID: PMC8894273 DOI: 10.5306/wjco.v13.i2.125] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/09/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.
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Affiliation(s)
- Hossein Haghbin
- Department of Gastroenterology, Ascension Providence Southfield, Southfield, MI 48075, United States
| | - Muhammad Aziz
- Department of Gastroenterology, University of Toledo Medical Center, Toledo, OH 43614, United States
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Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment. J Clin Med 2021; 10:jcm10102071. [PMID: 34066001 PMCID: PMC8150393 DOI: 10.3390/jcm10102071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/05/2021] [Accepted: 05/10/2021] [Indexed: 12/23/2022] Open
Abstract
Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.
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