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Chen B, Mao Y, Li J, Zhao Z, Chen Q, Yu Y, Yang Y, Dong Y, Lin G, Yao J, Lu M, Wu L, Bo Z, Chen G, Xie X. Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study. Comput Biol Med 2023; 167:107612. [PMID: 37939408 DOI: 10.1016/j.compbiomed.2023.107612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Even after curative resection, the prognosis for patients with intrahepatic cholangiocarcinoma (iCCA) remains disappointing due to the extremely high incidence of postoperative recurrence. METHODS A total of 280 iCCA patients following curative hepatectomy from three independent institutions were recruited to establish the retrospective multicenter cohort study. The very early recurrence (VER) of iCCA was defined as the appearance of recurrence within 6 months. The 3D tumor region of interest (ROI) derived from contrast-enhanced CT (CECT) was used for radiomics analysis. The independent clinical predictors for VER were histological stage, AJCC stage, and CA199 levels. We implemented K-means clustering algorithm to investigate novel radiomics-based subtypes of iCCA. Six types of machine learning (ML) algorithms were performed for VER prediction, including logistic, random forest (RF), neural network, bayes, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Additionally, six clinical ML (CML) models and six radiomics-clinical ML (RCML) models were developed to predict VER. Predictive performance was internally validated by 10-fold cross-validation in the training cohort, and further evaluated in the external validation cohort. RESULTS Approximately 30 % of patients with iCCA experienced VER with extremely discouraging outcome (Hazard ratio (HR) = 5.77, 95 % Confidence Interval (CI) = 3.73-8.93, P < 0.001). Two distinct iCCA subtypes based on radiomics features were identified, and subtype 2 harbored a higher proportion of VER (47.62 % Vs 25.53 %) and significant shorter survival time than subtype 1. The average AUC values of the CML and RCML models were 0.744 ± 0.018, and 0.900 ± 0.014 in the training cohort, and 0.769 ± 0.065 and 0.929 ± 0.027 in the external validation cohort, respectively. CONCLUSION Two radiomics-based iCCA subtypes were identified, and six RCML models were developed to predict VER of iCCA, which can be used as valid tools to guide individualized management in clinical practice.
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Affiliation(s)
- Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Yicheng Mao
- Department of Optometry and Ophthalmology College, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Jiacheng Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Zhengxiao Zhao
- Department of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310000, China
| | - Qiwen Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Yaoyao Yu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Yulong Dong
- Department of Oncology, The Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Ganglian Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Jiangqiao Yao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Mengmeng Lu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Lijun Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
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Fiz F, Rossi N, Langella S, Ruzzenente A, Serenari M, Ardito F, Cucchetti A, Gallo T, Zamboni G, Mosconi C, Boldrini L, Mirarchi M, Cirillo S, De Bellis M, Pecorella I, Russolillo N, Borzi M, Vara G, Mele C, Ercolani G, Giuliante F, Ravaioli M, Guglielmi A, Ferrero A, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model. Cancers (Basel) 2023; 15:4204. [PMID: 37686480 PMCID: PMC10486795 DOI: 10.3390/cancers15174204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023] Open
Abstract
Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical-radiomic model outperforms a purely clinical one (p < 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
| | - Noemi Rossi
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (N.R.); (F.I.)
| | - Serena Langella
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Andrea Ruzzenente
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Matteo Serenari
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (M.S.); (M.R.)
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Alessandro Cucchetti
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Teresa Gallo
- Department of Radiology, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (T.G.); (S.C.)
| | - Giulia Zamboni
- Department of Radiology, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (G.Z.); (M.B.)
| | - Cristina Mosconi
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (C.M.); (G.V.)
| | - Luca Boldrini
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy;
| | - Mariateresa Mirarchi
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Stefano Cirillo
- Department of Radiology, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (T.G.); (S.C.)
| | - Mario De Bellis
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Nadia Russolillo
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Martina Borzi
- Department of Radiology, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (G.Z.); (M.B.)
| | - Giulio Vara
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (C.M.); (G.V.)
| | - Caterina Mele
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Giorgio Ercolani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Matteo Ravaioli
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (M.S.); (M.R.)
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
| | - Alfredo Guglielmi
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Alessandro Ferrero
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (N.R.); (F.I.)
- CHDS—Center for Health Data Science, Human Technopole, 20157 Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, 24125 Bergamo, Italy
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Shah AA, Alturise F, Alkhalifah T, Faisal A, Khan YD. EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma. Genes (Basel) 2023; 14:genes14051104. [PMID: 37239464 DOI: 10.3390/genes14051104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/11/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.
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Affiliation(s)
- Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad 04408, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Amna Faisal
- Department of Computer Science, Bahria University, Lahore 54782, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore 54782, Pakistan
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Liu J, Liu M, Gong Y, Su S, Li M, Shu J. Prediction of angiogenesis in extrahepatic cholangiocarcinoma using MRI-based machine learning. Front Oncol 2023; 13:1048311. [PMID: 37274267 PMCID: PMC10233135 DOI: 10.3389/fonc.2023.1048311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 04/28/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose Reliable noninvasive method to preoperative prediction of extrahepatic cholangiocarcinoma (eCCA) angiogenesis are needed. This study aims to develop and validate machine learning models based on magnetic resonance imaging (MRI) for predicting vascular endothelial growth factor (VEGF) expression and the microvessel density (MVD) of eCCA. Materials and methods In this retrospective study from August 2011 to May 2020, eCCA patients with pathological confirmation were selected. Features were extracted from T1-weighted, T2-weighted, and diffusion-weighted images using the MaZda software. After reliability testing and feature screening, retained features were used to establish classification models for predicting VEGF expression and regression models for predicting MVD. The performance of both models was evaluated respectively using area under the curve (AUC) and Adjusted R-Squared (Adjusted R2). Results The machine learning models were developed in 100 patients. A total of 900 features were extracted and 77 features with intraclass correlation coefficient (ICC) < 0.75 were eliminated. Among all the combinations of data preprocessing methods and classification algorithms, Z-score standardization + logistic regression exhibited excellent ability both in the training cohort (average AUC = 0.912) and the testing cohort (average AUC = 0.884). For regression model, Z-score standardization + stochastic gradient descent-based linear regression performed well in the training cohort (average Adjusted R2 = 0.975), and was also better than the mean model in the test cohort (average Adjusted R2 = 0.781). Conclusion Two machine learning models based on MRI can accurately predict VEGF expression and the MVD of eCCA respectively.
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Affiliation(s)
- Jiong Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Mali Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Yaolin Gong
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Man Li
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Shanghai, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
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Brunese MC, Fantozzi MR, Fusco R, De Muzio F, Gabelloni M, Danti G, Borgheresi A, Palumbo P, Bruno F, Gandolfo N, Giovagnoni A, Miele V, Barile A, Granata V. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma. Diagnostics (Basel) 2023; 13:diagnostics13081488. [PMID: 37189589 DOI: 10.3390/diagnostics13081488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. METHODS The PubMed database was searched for papers published in the English language no earlier than October 2022. RESULTS We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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Affiliation(s)
- Maria Chiara Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy
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Qian X, Zhou C, Wang F, Lu X, Zhang Y, Chen L, Zeng M. Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics. Radiol Med 2023; 128:274-288. [PMID: 36773271 PMCID: PMC10020304 DOI: 10.1007/s11547-023-01597-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/10/2023] [Indexed: 02/12/2023]
Abstract
PURPOSE Incidence and mortality of intrahepatic cholangiocarcinoma (ICC) have been increasing over the past few decades, and Ki67 is an adverse prognostic predictor and an attractive therapeutic target for ICC patients. Thus, we aim to develop and validate a combined Ki67 prediction model for ICC patients. MATERIALS AND METHODS Preoperative contrast-enhanced MR images were collected from 178 patients with postoperative pathologically confirmed ICC, and randomly divided into training and validation cohorts in a ratio of 7:3 (124:54). A time-independent test cohort of 49 ICC patients was used for validation. Independent clinicoradiological features of Ki67 status were determined by multivariate analysis. Optimal radiomics features were selected by least absolute shrinkage and selection operator logistic regression and linear discriminant analysis was used to construct combined models. The prediction efficacy of combined model was assessed by receiver operating characteristics curve, and verified by its calibration, decision and clinical impact curves. RESULTS HBV (p = 0.022), arterial rim enhancement (p = 0.006) and enhancement pattern (p = 0.012) are independent clinicoradiological features. The radiomics model achieves good prediction efficacy in the training cohort (AUC = 0.860) and validation cohort (AUC = 0.843). The combined Ki67 prediction model incorporates clinicoradiological and radiomics features, and it yields desirable predictive efficiency in test cohort (AUC = 0.815). Decision curves and clinical impact curves further validate that the combined Ki67 prediction model can achieve net benefits in clinical work. CONCLUSION The combined Ki67 model incorporating HBV, arterial rim enhancement, enhancement pattern and radiomics features is a potential biomarker in Ki67 prediction and stratification.
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Affiliation(s)
- Xianling Qian
- grid.413087.90000 0004 1755 3939Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
| | - Changwu Zhou
- grid.413087.90000 0004 1755 3939Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
| | - Fang Wang
- Shanghai United Imaging Intelligence Co., Ltd, No.701 Yunjin Rd, Shanghai, 200232 China
| | - Xin Lu
- grid.413087.90000 0004 1755 3939Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
| | - Yunfei Zhang
- grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.497849.fCentral Research Institute, United Imaging Healthcare, No.2258 Chengbei Rd, Shanghai, 201807 China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd, No.701 Yunjin Rd, Shanghai, 200232 China
| | - Mengsu Zeng
- grid.413087.90000 0004 1755 3939Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, No.180 Fenglin Rd, Shanghai, 200032 China
- grid.413087.90000 0004 1755 3939Department of Cancer Center, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai, 200032 China
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Zhao QX, He XL, Wang K, Cheng ZG, Han ZY, Liu FY, Yu XL, Hui Z, Yu J, Chao A, Liang P. Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis. Eur Radiol 2023; 33:1895-905. [PMID: 36418624 DOI: 10.1007/s00330-022-09203-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/19/2022] [Accepted: 09/25/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM). METHODS Between January 2010 and May 2019, a total of 207 consecutive patients with CRLM with 13,248 slice images at three dynamic phases who received CEUS within 2 weeks before TA were retrospectively enrolled in two centres (153 for the training cohort (TC), 32 for the internal test cohort (ITC), and 22 for the external test cohort (ETC)). Clinical and CEUS data were used to develop and validate the clinical model, DL model, and DL combining with clinical (DL-C) model to predict ER after TA. The performance of these models was compared by the receiver operating characteristic curve (ROC) with the DeLong test. RESULTS After a median follow-up of 56 months, 49% (99/207) of patients experienced ER. Three key clinical features (preoperative chemotherapy (PC), lymph node metastasis of the primary colorectal cancer (LMPCC), and T stage) were used to develop the clinical model. The DL model yielded better performance than the clinical model in the ETC (AUC: 0.67 for the clinical model, 0.76 for the DL model). The DL-C model significantly outperformed the clinical model and DL model (AUC: 0.78 for the DL-C model in the ETC; both, p < 0.001). CONCLUSIONS The model based on CEUS can achieve satisfactory prediction and assist physicians during the therapeutic decision-making process in clinical practice. KEY POINTS • This is an exploratory study in which ablation-related contrast-enhanced ultrasound (CEUS) data from consecutive patients with colorectal cancer liver metastasis (CRLM) were collected simultaneously at multiple institutions. • The deep learning combining with clinical (DL-C) model provided desirable performance for the prediction of early recurrence (ER) after thermal ablation (TA). • The DL-C model based on CEUS provides guidance for TA indication selection and making therapeutic decisions.
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Chen P, Yang Z, Zhang H, Huang G, Li Q, Ning P, Yu H. Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend. Front Oncol 2023; 13:1133867. [PMID: 37035147 PMCID: PMC10076873 DOI: 10.3389/fonc.2023.1133867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.
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Affiliation(s)
- Pengyu Chen
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Zhenwei Yang
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Haofeng Zhang
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Guan Huang
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingshan Li
- Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Peigang Ning
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Haibo Yu
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
- Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, Zhengzhou, China
- *Correspondence: Haibo Yu,
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Yao F, Ding J, Lin F, Xu X, Jiang Q, Zhang L, Fu Y, Yang Y, Lan L. Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer. Br J Radiol 2022; 95:20211332. [PMID: 35612547 PMCID: PMC10162053 DOI: 10.1259/bjr.20211332] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. METHODS Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. RESULTS The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77-0.90) and 0.82 (95% CI: 0.71-0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. CONCLUSIONS The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC. ADVANCES IN KNOWLEDGE This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.
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Affiliation(s)
- Fei Yao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jie Ding
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Lin
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaomin Xu
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qi Jiang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Li Zhang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanqi Fu
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Li Lan
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Chen Y, Lu Q, Zhu Y, Huang B, Dong Y, Wang W. Prediction of Microvascular Invasion in Combined Hepatocellular-Cholangiocarcinoma Based on Pre-operative Clinical Data and Contrast-Enhanced Ultrasound Characteristics. Ultrasound Med Biol 2022; 48:1190-1201. [PMID: 35397928 DOI: 10.1016/j.ultrasmedbio.2022.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/27/2022] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
The goal of the study described here was to define the predictive value of pre-operative clinical information and contrast-enhanced ultrasound (CEUS) imaging characteristics in combined hepatocellular-cholangiocarcinoma (CHC) patients with microvascular invasion (MVI). Seventy-six patients with pathologically confirmed CHC were enrolled in this study, comprising 18 patients with MVI-positive status and 58 with MVI-negative CHC nodules. The pre-operative clinical data and CEUS imaging features were retrospectively analyzed. Univariate and multivariate analyses were performed to identify the potential predictors of MVI in CHC. Recurrence-free survival (RFS) after hepatectomy was compared between patients with different MVI status using the log-rank test and Kaplan-Meier survival curves. Univariate analysis indicated that the following parameters of patients with CHC significantly differed between the MVI-positive and MVI-negative groups (p<0.05): tumor size, α-fetoprotein ≥400 ng/mL, enhancement patterns in arterial phase and marked washout during the portal venous phase on CEUS. On multivariate logistic regression analysis, only the CEUS characteristics of heterogeneous enhancement (odds ratio = 6.807; 95% confidence interval [CI]: 1.099, 42.147; p = 0.039) and marked washout (odds ratio = 4.380; 95% CI: 1.050,18.270; p = 0.043) were identified as independent predictors of MVI in CHC. The combination of the two risk factors in predicting MVI achieved a better diagnostic performance than each parameter alone, with an area under the receiver operating characteristic curve of 0.736 (0.622, 0.830). After hepatectomy, CHC patients with MVI exhibited earlier recurrence compared with those without MVI (hazard ratio = 1.859; 95% CI: 0.8699-3.9722, p = 0.046). The CEUS imaging features of heterogeneous enhancement in the arterial phase and marked washout during the portal venous phase were the potential predictors of MVI in CHC. Aside from that, CHC patients with MVI had an earlier recurrence rate than those without MVI after surgery.
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Affiliation(s)
- Yanling Chen
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qing Lu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuli Zhu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Beijian Huang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Institute of Ultrasound Medicine and Engineering, Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Institute of Ultrasound Medicine and Engineering, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China.
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Zhan PC, Lyu PJ, Li Z, Liu X, Wang HX, Liu NN, Zhang Y, Huang W, Chen Y, Gao JB. CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma. Front Oncol 2022; 12:900478. [PMID: 35795043 PMCID: PMC9252420 DOI: 10.3389/fonc.2022.900478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively. Materials and Methods From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed. Results Finally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility. Conclusion We developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Pei-jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenpeng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
- *Correspondence: Jian-bo Gao, ; Pei-jie Lyu,
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Li MD, Lu XZ, Liu JF, Chen B, Xu M, Xie XY, Lu MD, Kuang M, Wang W, Shen SL, Chen LD. Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound-Based Radiographic-Radiomics Signature. J Ultrasound Med 2022; 41:1483-1495. [PMID: 34549829 DOI: 10.1002/jum.15833] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/03/2021] [Accepted: 08/22/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic-radiomics signatures. METHODS Between April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)-related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time-dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic-radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C-index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms. RESULTS The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76-0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19-9, sex, ascites, radiomics signature, and radiographic signature had C-indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C-index: 0.75 versus 0.67, P = .004) and a higher C-index than the radiomics nomograms (0.75 versus 0.68, P = .044). CONCLUSIONS The preoperative nomogram integrated with the radiographic-radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.
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Affiliation(s)
- Ming-De Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-Zhou Lu
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Jun-Feng Liu
- Department of Pathology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Bin Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Xu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-Yan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-De Lu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shun-Li Shen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
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Hou S, Li Y, Bai M, Sun M, Liu W, Wang C, Tetik H, Lin D. Phase Prediction of High-Entropy Alloys by Integrating Criterion and Machine Learning Recommendation Method. Materials (Basel) 2022; 15:3321. [PMID: 35591654 DOI: 10.3390/ma15093321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/28/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023]
Abstract
The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML algorithms based on the same dataset vary significantly. Therefore, selection of an efficient ML algorithm would significantly reduce the number and cost of the experiments. In this work, phase prediction of HEAs (PPH) is proposed by integrating criterion and machine learning recommendation method (MLRM). First, a meta-knowledge table based on characteristics of HEAs and performance of candidate algorithms is established, and meta-learning based on the meta-knowledge table is adopted to recommend an algorithm with desirable accuracy. Secondly, an MLRM based on improved meta-learning is engineered to recommend a more desirable algorithm for phase prediction. Finally, considering poor interpretability and generalization of single ML algorithms, a PPH combining the advantages of MLRM and criterion is proposed to improve the accuracy of phase prediction. The PPH is validated by 902 samples from 12 datasets, including 405 quinary HEAs, 359 senary HEAs, and 138 septenary HEAs. The experimental results shows that the PPH achieves performance than the traditional meta-learning method. The average prediction accuracy of PPH in all, quinary, senary, and septenary HEAs is 91.6%, 94.3%, 93.1%, and 95.8%, respectively.
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Fiz F, Jayakody Arachchige VS, Gionso M, Pecorella I, Selvam A, Wheeler DR, Sollini M, Viganò L. Radiomics of Biliary Tumors: A Systematic Review of Current Evidence. Diagnostics (Basel) 2022; 12:diagnostics12040826. [PMID: 35453878 PMCID: PMC9024804 DOI: 10.3390/diagnostics12040826] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729−0.900, accuracy = 0.69−0.83), tumor grading (AUC = 0.680−0.890, accuracy = 0.70−0.82), and survival (C-index = 0.673−0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
| | - Visala S Jayakody Arachchige
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Matteo Gionso
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Apoorva Selvam
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Dakota Russell Wheeler
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
- Correspondence: ; Tel.: +39-02-8224-7361
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Granata V, Fusco R, Belli A, Borzillo V, Palumbo P, Bruno F, Grassi R, Ottaiano A, Nasti G, Pilone V, Petrillo A, Izzo F. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect Agent Cancer 2022; 17:13. [PMID: 35346300 PMCID: PMC8961950 DOI: 10.1186/s13027-022-00429-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023] Open
Abstract
Background This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC). Methods Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.
Results We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs. Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance. Conclusions Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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18
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Chen Y, Liu H, Zhang J, Wu Y, Zhou W, Cheng Z, Lou J, Zheng S, Bi X, Wang J, Guo W, Li F, Wang J, Zheng Y, Li J, Cheng S, Zeng Y, Liu J. Prognostic value and predication model of microvascular invasion in patients with intrahepatic cholangiocarcinoma: a multicenter study from China. BMC Cancer 2021; 21:1299. [PMID: 34863147 PMCID: PMC8645153 DOI: 10.1186/s12885-021-09035-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/16/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND At present, hepatectomy is still the most common and effective treatment method for intrahepatic cholangiocarcinoma (ICC) patients. However, the postoperative prognosis is poor. Therefore, the prognostic factors for these patients require further exploration. Whether microvascular invasion (MVI) plays a crucial role in the prognosis of ICC patients is still unclear. Moreover, few studies have focused on preoperative predictions of MVI in ICC patients. METHODS Clinicopathological data of 704 ICC patients after curative resection were retrospectively collected from 13 hospitals. Independent risk factors were identified by the Cox or logistic proportional hazards model. In addition, the survival curves of the MVI-positive and MVI-negative groups before and after matching were analyzed. Subsequently, 341 patients from a single center (Eastern Hepatobiliary Hospital) in the above multicenter retrospective cohort were used to construct a nomogram prediction model. Then, the model was evaluated by the index of concordance (C-Index) and the calibration curve. RESULTS After propensity score matching (PSM), Child-Pugh grade and MVI were independent risk factors for overall survival (OS) in ICC patients after curative resection. Major hepatectomy and MVI were independent risk factors for recurrence-free survival (RFS). The survival curves of OS and RFS before and after PSM in the MVI-positive groups were significantly different compared with those in the MVI-negative groups. Multivariate logistic regression results demonstrated that age, gamma-glutamyl transpeptidase (GGT), and preoperative image tumor number were independent risk factors for the occurrence of MVI. Furthermore, the prediction model in the form of a nomogram was constructed, which showed good prediction ability for both the training (C-index = 0.7622) and validation (C-index = 0.7591) groups, and the calibration curve showed good consistency with reality. CONCLUSION MVI is an independent risk factor for the prognosis of ICC patients after curative resection. Age, GGT, and preoperative image tumor number were independent risk factors for the occurrence of MVI in ICC patients. The prediction model constructed further showed good predictive ability in both the training and validation groups with good consistency with reality.
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Affiliation(s)
- Yifan Chen
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China
| | - Hongzhi Liu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China
| | - Jinyu Zhang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China
| | - Yijun Wu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China
| | - Weiping Zhou
- Department of Hepatobiliary Surgery III, Eastern Hepatobiliary Surgery Hospital, Secondary Military Medical University, Shanghai, China
| | - Zhangjun Cheng
- Department of Hepatobiliary Surgery, The Affiliated Zhongda Hospital of Southeast University, Nanjing, 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
| | - Yongyi Zeng
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China. .,Liver Diseases Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
| | - Jingfeng Liu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, Fujian Province, People's Republic of China.
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Rimini M, Puzzoni M, Pedica F, Silvestris N, Fornaro L, Aprile G, Loi E, Brunetti O, Vivaldi C, Simionato F, Zavattari P, Scartozzi M, Burgio V, Ratti F, Aldrighetti L, Cascinu S, Casadei-Gardini A. Cholangiocarcinoma: new perspectives for new horizons. Expert Rev Gastroenterol Hepatol 2021; 15:1367-1383. [PMID: 34669536 DOI: 10.1080/17474124.2021.1991313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Biliary tract cancer represents a heterogeneous group of malignancies characterized by dismal prognosis and scarce therapeutic options. AREA COVERED In the last years, a growing interest in BTC pathology has emerged, thus highlighting a significant heterogeneity of the pathways underlying the carcinogenesis process, from both a molecular and genomic point of view. A better understanding of these differences is mandatory to deepen the behavior of this complex disease, as well as to identify new targetable target mutations, with the aim to improve the survival outcomes. The authors decided to provide a comprehensive overview of the recent highlights on BTCs, with a special focus on the genetic, epigenetic and molecular alterations, which may have an interesting clinical application in the next future. EXPERT OPINION In the last years, the efforts resulted from international collaborations have led to the identification of new promising targets for precision medicine approaches in the BTC setting. Further investigations and prospective trials are needed, but the hope is that these new knowledge in cooperation with the new technologies and procedures, including bio-molecular and genomic analysis as well radiomic studies, will enrich the therapeutic armamentarium thus improving the survival outcomes in a such lethal and complex disease.
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Affiliation(s)
- Margherita Rimini
- Department of Oncology and Hematology, Division of Oncology, University of Modena and Reggio Emilia, Modena, Italy
| | - Marco Puzzoni
- Medical Oncology, University and University Hospital of Cagliari, Italy
| | - Federica Pedica
- Department of Pathology, San Raffaele Scientific Institute, Milan, Italy
| | - Nicola Silvestris
- Department of oncology, Instituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Istituto Tumori "Giovanni Paolo Ii" of Bari, Bari, Italy.,Department of Biomedical Sciences and Human Oncology, Aldo Moro University of Bari, Bari, Italy
| | - Lorenzo Fornaro
- Department of medical oncology, U.O. Oncologia Medica 2 Universitaria, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Giuseppe Aprile
- Department of Oncology, San Bortolo General Hospital, Azienda ULSS8 Berica, Vicenza, Italy
| | - Eleonora Loi
- Department of Biomedical Sciences, Unit of Biology and Genetics, University of Cagliari, Cagliari, Italy
| | - Oronzo Brunetti
- Department of oncology, Instituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Istituto Tumori "Giovanni Paolo Ii" of Bari, Bari, Italy
| | - Caterina Vivaldi
- Department of medical oncology, U.O. Oncologia Medica 2 Universitaria, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Francesca Simionato
- Department of Oncology, San Bortolo General Hospital, Azienda ULSS8 Berica, Vicenza, Italy
| | - Patrizia Zavattari
- Department of Biomedical Sciences, Unit of Biology and Genetics, University of Cagliari, Cagliari, Italy
| | - Mario Scartozzi
- Medical Oncology, University and University Hospital of Cagliari, Italy
| | - Valentina Burgio
- Department of Oncology, IRCCS San Raffaele Scientific Institute Hospital, Milan, Italy
| | - Francesca Ratti
- Hepatobiliary Surgery Division, IRCCS San Raffaele and Vita-Salute University, Italy
| | - Luca Aldrighetti
- Hepatobiliary Surgery Division, IRCCS San Raffaele and Vita-Salute University, Italy
| | - Stefano Cascinu
- Department of Oncology, IRCCS San Raffaele Scientific Institute Hospital, Milan, Italy
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Li Q, Che F, Wei Y, Jiang HY, Zhang Y, Song B. Role of noninvasive imaging in the evaluation of intrahepatic cholangiocarcinoma: from diagnosis and prognosis to treatment response. Expert Rev Gastroenterol Hepatol 2021; 15:1267-1279. [PMID: 34452581 DOI: 10.1080/17474124.2021.1974294] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Intrahepatic cholangiocarcinoma is the second most common liver cancer. Desmoplastic stroma may be revealed as distinctive histopathologic findings favoring intrahepatic cholangiocarcinoma. Meanwhile, a range of imaging manifestations is often accompanied with rich desmoplastic stroma in intrahepatic cholangiocarcinoma, which can indicate large bile duct ICC, and a higher level of cancer-associated fibroblasts with poor prognosis and weak treatment response. AREAS COVERED We provide a comprehensive review of current state-of-the-art and recent advances in the imaging evaluation for diagnosis, staging, prognosis and treatment response of intrahepatic cholangiocarcinoma. In addition, we discuss precursor lesions, cells of origin, molecular mutation, which would cause the different histological classification. Moreover, histological classification and tumor microenvironment, which are related to the proportion of desmoplastic stroma with many imaging manifestations, would be also discussed. EXPERT OPINION The diagnosis, prognosis, treatment response of intrahepatic cholangiocarcinoma may be revealed as the presence and the proportion of desmoplastic stroma with a range of imaging manifestations. With the utility of radiomics and artificial intelligence, imaging is helpful for ICC evaluation. Multicentre, large-scale, prospective studies with external validation are in need to develop comprehensive prediction models based on clinical data, imaging findings, genetic parameters, molecular, metabolic, and immune biomarkers.
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Affiliation(s)
- Qian Li
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Feng Che
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yi Wei
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Han-Yu Jiang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yun Zhang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
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21
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Yao F, Ding J, Hu Z, Cai M, Liu J, Huang X, Zheng R, Lin F, Lan L. Ultrasound-based radiomics score: a potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer. Abdom Radiol (NY) 2021; 46:4936-45. [PMID: 34120235 DOI: 10.1007/s00261-021-03163-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE More than 80% of patients with ovarian epithelial cancer (OEC) show complete remission after initial treatment but eventually experience recurrence of the disease. This study aimed to develop a radiomics signature to identify a new prognostic indicator based on preoperative ultrasound imaging. METHODS A total of 111 patients with OEC who underwent transvaginal ultrasound before surgery were included. Of these, 76 were divided into the training cohort and 35 into the test cohort. We defined the region of interest (ROI) of the tumor by manually drawing the tumor contour on the ultrasound image of the lesion. The radiomics features were extracted from ultrasound images. The radiomics score (Rad-Score) was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Combined with the ultrasound radiomics features, significant clinical variables were also used to establish predictive models for 5-year progression-free survival (PFS) prediction. The efficiency of the model was evaluated using the area under the curve (AUC). Kaplan-Meier analysis was used to evaluate the association between the Rad-Score and PFS. RESULTS The combined model was superior to the clinical and Rad-Score models in estimating 5-year PFS and achieved an AUC of 0.868 (95%CI 0.766-0.971) in the training cohort. The Rad-Score was negatively correlated with prognosis in the training and test cohorts. CONCLUSIONS The combined model that incorporated both clinical parameters and ultrasound radiomics features achieved a good prognosis in patients with OEC, which might aid clinical decision-making.
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22
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Wang S, Liu X, Zhao J, Liu Y, Liu S, Liu Y, Zhao J. Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review. Comput Methods Programs Biomed 2021; 208:106265. [PMID: 34311415 DOI: 10.1016/j.cmpb.2021.106265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Cholangiocarcinoma (CCA) is one of the most aggressive human malignant tumors and is becoming one of the main factors of death and disability globally. Specifically, 60% to 70% of CCA patients were diagnosed with local invasion or distant metastasis and lost the chance of radical operation. The overall median survival time was less than 12 months. As a non-invasive diagnostic technology, medical imaging consisting of computed tomography (CT) imaging, magnetic resonance imaging (MRI), and ultrasound (US) imaging, is the most effectively and commonly used method to detect CCA. The computer auxiliary diagnosis (CAD) system based on medical imaging is helpful for rapid diagnosis and provides credible "second opinion" for specialists. The purpose of this review is to categorize and review the CAD technique of detecting CCA based on medical imaging. METHODS This work applies a four-level screening process to choose suitable publications. 125 research papers published in different academic research databases were selected and analyzed according to specific criteria. From the five steps of medical image acquisition, processing, analysis, understanding and verification of CAD combined with artificial intelligence algorithms, we obtain the most advanced insights related to CCA detection. RESULTS This work provides a comprehensive analysis and comparison analysis of the current CAD systems of detecting CCA. After careful investigation, we find that the main detection methods are traditional machine learning method and deep learning method. For the detection, the most commonly used method is semi-automatic segmentation algorithm combined with support vector machine classifier method, combination of which has good detection performance. The end-to-end training mode makes deep learning method more and more popular in CAD systems. However, due to the limited medical training data, the accuracy of deep learning method is unsatisfactory. CONCLUSIONS Based on analysis of artificial intelligence methods applied in CCA, this work is expected to be truly applied in clinical practice in the future to improve the level of clinical diagnosis and treatment of it. This work concludes by providing a prediction of future trends, which will be of great significance for researchers in the medical imaging of CCA and artificial intelligence.
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Affiliation(s)
- Shiyu Wang
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xiang Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Jingwen Zhao
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Yiwen Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Shuhong Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Yisi Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Jingmin Zhao
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China.
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Huang X, Shu J, Yan Y, Chen X, Yang C, Zhou T, Li M. Feasibility of magnetic resonance imaging-based radiomics features for preoperative prediction of extrahepatic cholangiocarcinoma stage. Eur J Cancer 2021; 155:227-35. [PMID: 34391055 DOI: 10.1016/j.ejca.2021.06.053] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/11/2021] [Accepted: 06/29/2021] [Indexed: 01/03/2023]
Abstract
AIM The aim of this study is to develop and test radiomics models based on magnetic resonance imaging (MRI) to preoperatively and respectively predict the T stage, perineural invasion, and microvascular invasion of extrahepatic cholangiocarcinoma (eCCA) through a non-invasive approach. METHODS This research included 101 eCCA patients (29-83 years; 45 females and 56 males) between August 2011 and December 2019. Radiomics features were retrospectively extracted from T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map using MaZda software. The region of interest was manually delineated in the largest section on four MRI images as ground truth while keeping 1-2 mm margin to tumor border, respectively. Pretreatment, dimension reduction method, and classifiers were used to establish radiomics signatures for assessing three pathological characteristics of eCCA. Finally, independent training and testing datasets were used to assess radiomics signature performance based on receiver operating characteristic curve analysis, accuracy, precision, sensitivity, and specificity. RESULTS This study extracted 1208 radiomics features from four MRI images of each patient. The best performing radiomics signatures for assessing the T stage, perineural invasion, and microvascular invasion were respectively produced by L1_normalization + linear discriminant analysis (LDA) + logistic regression, Box_Cox transformer + LDA + K-nearest neighbor, and L2_normalization + LDA + AdaBoost. The area under the curve values of the radiomics signatures for predicting the training and testing cohorts in each subgroup were respectively 1 and 0.962 (T stage), 1 and 1 (both perineural invasion and microvascular invasion). CONCLUSION These proposed radiomic models based on MR images had powerful performance and high potential in predicting T stage, perineural, and microvascular invasion of eCCA. REPORTING GUIDELINES/RESEARCH DESIGN Prognostic study.
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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Abstract
Introduction: Biliary Tract Cancer (BTC) is a heterogeneous group of malignant neoplasms with a complex molecular pathogenesis. The prognosis of metastatic disease is dramatically dismal and therapeutic options are scarce. Systemic chemotherapy is the gold standard for the metastatic disease. However, because of the disappointing results with conventional chemotherapy, investigators have turned to new biological therapeutic options targeting the main molecular pathways, neo-angiogenesis, involved in the disease pathogenesis.Areas covered: This paper examines the rationale of using antiangiogenic therapies in this setting, evaluates the therapeutic implications, and highlights ongoing studies and future perspectives. A Pubmed systematic review of preclinical and clinical data was performed which enabled the composition of this paper.Expert opinion: Amore in-depth understanding of the interplay between the neo-angiogenesis pathways, and the microenvironment will could propel the design new therapeutic strategies. Nowadays, the combination of antiangiogenic drugs and immune check-point inhibitors looks promising, but further, more comprehensive data are necessary to gain afuller picture. In an era of novel technologies and techniques, which includes radiomics, the challenge is to identify the biomarkers of response to antiangiogenic drugs which will permit the selection of patients that are more likely to respond to antiangiogenic therapies.
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Affiliation(s)
- Margherita Rimini
- Department of Oncology and Hematology, Division of Oncology, University Hospital Modena, Modena, Italy
| | - Andrea Casadei-Gardini
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.,Unit of Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Zhao J, Zhang W, Fan CL, Zhang J, Yuan F, Liu SY, Li FY, Song B. Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study. Eur J Radiol 2021; 138:109631. [PMID: 33711571 DOI: 10.1016/j.ejrad.2021.109631] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/18/2021] [Accepted: 03/02/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA). MATERIALS AND METHODS A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve. RESULTS The clinical model (Modelclinic) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images. The model yield the best performance (Modelclinic&AP&PVP) was build using three independent variables, SignatureAP and SignaturePVP. In training and testing cohorts, the concordance indexes (C-indexes) of Modelclinic were 0.846 (95 % CI, 0.735-0.957) and 0.755 (95 % CI, 0.540-969), and Modelclinic&AP&PVP achieved C-indexes of 0.962 (95 % CI, 0.905-1) and 0.814 (95 % CI, 0.569-1). Both Modelclinic and Modelclinic&AP&PVP outperformed the TNM staging system. Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis for Modelclinic and Modelclinic&AP&PVP. CONCLUSION Two preoperative nomograms were constructed to predict 1-, 3-, and 5-years survival for individual pCCA patients, demonstrating the potential for clinical application to assist decision-making.
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Affiliation(s)
- Jian Zhao
- Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China; Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, PR China
| | - Wei Zhang
- Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China; Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, PR China
| | - Cheng-Lin Fan
- Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, PR China
| | - Jun Zhang
- Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China
| | - Fang Yuan
- Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China
| | - Si-Yun Liu
- GE Healthcare (China), 100176, Beijing, PR China
| | - Fu-Yu Li
- Department of Biliary Surgery, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China.
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Wiskin J, Malik B, Borup D, Pirshafiey N, Klock J. Full wave 3D inverse scattering transmission ultrasound tomography in the presence of high contrast. Sci Rep 2020; 10:20166. [PMID: 33214569 DOI: 10.1038/s41598-020-76754-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/02/2020] [Indexed: 12/29/2022] Open
Abstract
We present here a quantitative ultrasound tomographic method yielding a sub-mm resolution, quantitative 3D representation of tissue characteristics in the presence of high contrast media. This result is a generalization of previous work where high impedance contrast was not present and may provide a clinically and laboratory relevant, relatively inexpensive, high resolution imaging method for imaging in the presence of bone. This allows tumor, muscle, tendon, ligament or cartilage disease monitoring for therapy and general laboratory or clinical settings. The method has proven useful in breast imaging and is generalized here to high-resolution quantitative imaging in the presence of bone. The laboratory data are acquired in ~ 12 min and the reconstruction in ~ 24 min-approximately 200 times faster than previously reported simulations in the literature. Such fast reconstructions with real data require careful calibration, adequate data redundancy from a 2D array of 2048 elements and a paraxial approximation. The imaging results show that tissue surrounding the high impedance region is artifact free and has correct speed of sound at sub-mm resolution.
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Yao X, Huang X, Yang C, Hu A, Zhou G, Lei J, Shu J. A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model. JMIR Med Inform 2020; 8:e23578. [PMID: 33016889 PMCID: PMC7573697 DOI: 10.2196/23578] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 09/18/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. OBJECTIVE The objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. METHODS For this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). RESULTS A radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. CONCLUSIONS The MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.
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Affiliation(s)
- Xiaopeng Yao
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China
| | - Xinqiao Huang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Anbin Hu
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China
| | - Guangjin Zhou
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Jianbo Lei
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Center for Medical Informatics/Institute of Medical Technology, Peking University, Beijing, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Chen L, Zeng F, Yao L, Fang T, Liao M, Long J, Xiao L, Deng G. Nomogram based on inflammatory indices for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma. Cancer Med 2020; 9:1451-1461. [PMID: 31903730 PMCID: PMC7013079 DOI: 10.1002/cam4.2823] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 12/18/2019] [Accepted: 12/22/2019] [Indexed: 12/12/2022] Open
Abstract
Objective To establish nomogram based on inflammatory indices for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC). Methods A cohort of 422 patients with HCC or ICC hospitalized at Xiangya Hospital between January 2014 and December 2018 was included in the study. Univariate and multivariate analysis was performed to identify the independent differential factors. Through combining these independent differential factors, a nomogram was established for differential diagnosis between ICC and HCC. The accuracy of nomogram was evaluated by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). The results were validated using a prospective study on 98 consecutive patients operated on from January 2019 to November 2019 at the same institution. Results Sex (OR = 9.001, 95% CI: 3.268‐24.792, P < .001), hepatitis (OR = 0.323, 95% CI: 0.121‐0.860, P = .024), alpha‐fetoprotein (AFP) (OR = 0.997, 95% CI: 0.995‐1.000, P = .046), carbohydrate antigen 19‐9 (CA199) (OR = 1.016, 95% CI: 1.007‐1.025, P < .001), and aspartate transaminase‐to‐neutrophil ratio index (ANRI) (OR = 0.904, 95% CI: 0.843‐0.969, P = .004) were the independent differential factors for ICC. Nomogram was established with well‐fitted calibration curves through incorporating these 5 factors. Comparing model 1 including gender, hepatitis, AFP, and CA199 (C index = 0.903, 95% CI: 0.849‐0.957) and model 2 enrolling AFP and CA199 (C index = 0.850, 95% CI: 0.791‐0.908), the nomogram showed a better discrimination between ICC and HCC, with a C index of 0.920 (95% CI, 0.872‐0.968). The results were consistent in the validation cohort. DCA also confirmed the conclusion. Conclusion A nomogram was established for the differential diagnosis between ICC and HCC preoperatively, and better therapeutic choice would be made if it was applied in clinical practice.
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Affiliation(s)
- Lang Chen
- Xiangya Hospital, Central South University, Changsha, China
| | - Furong Zeng
- Xiangya Hospital, Central South University, Changsha, China
| | - Lei Yao
- Xiangya Hospital, Central South University, Changsha, China
| | - Tongdi Fang
- Xiangya Hospital, Central South University, Changsha, China
| | - Mengting Liao
- Xiangya Hospital, Central South University, Changsha, China
| | - Jing Long
- Xiangya Hospital, Central South University, Changsha, China
| | - Liang Xiao
- Xiangya Hospital, Central South University, Changsha, China
| | - Guangtong Deng
- Xiangya Hospital, Central South University, Changsha, China
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Bing MMD, Shaobo DMD, Ruiqing LMD, Na LP, Yaqiong LP, Lianzhong ZMD. The Roles of Ultrasound-Based Radiomics In Precision Diagnosis and Treatment of Different Cancers: A Literature Review. Advanced Ultrasound in Diagnosis and Therapy 2020. [DOI: 10.37015/audt.2020.200051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
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