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Mirza-Aghazadeh-Attari M, Afyouni S, Zandieh G, Yazdani Nia I, Mohseni A, Borhani A, Madani SP, Shahbazian H, Ansari G, Kim A, Kamel IR. Utilization of Radiomics Features Extracted From Preoperative Medical Images to Detect Metastatic Lymph Nodes in Cholangiocarcinoma and Gallbladder Cancer Patients: A Systemic Review and Meta-analysis. J Comput Assist Tomogr 2024; 48:184-193. [PMID: 38013233 DOI: 10.1097/rct.0000000000001557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVES This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer. METHODS Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability. RESULTS Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively). CONCLUSIONS The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.
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Affiliation(s)
| | - Shadi Afyouni
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Ghazal Zandieh
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Iman Yazdani Nia
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Alireza Mohseni
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Ali Borhani
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Seyedeh Panid Madani
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Haneyeh Shahbazian
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Golnoosh Ansari
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
| | - Amy Kim
- Department of Medicine, Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ihab R Kamel
- From the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital
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Gupta P, Basu S, Arora C. Applications of artificial intelligence in biliary tract cancers. Indian J Gastroenterol 2024:10.1007/s12664-024-01518-0. [PMID: 38427281 DOI: 10.1007/s12664-024-01518-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/29/2023] [Indexed: 03/02/2024]
Abstract
Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Zhan PC, Yang T, Zhang Y, Liu KY, Li Z, Zhang YY, Liu X, Liu NN, Wang HX, Shang B, Chen Y, Jiang HY, Zhao XT, Shao JH, Chen Z, Wang XD, Wang K, Gao JB, Lyu PJ. Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study. Eur Radiol 2024; 34:1280-1291. [PMID: 37589900 DOI: 10.1007/s00330-023-10108-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/07/2023] [Accepted: 06/29/2023] [Indexed: 08/18/2023]
Abstract
OBJECTIVES To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Ke-Yan Liu
- Zhengzhou University Medical College, Zhengzhou, 450052, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Yu-Yuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Bo Shang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiang-Tian Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jing-Hai Shao
- Department of Radiology, He Nan Sui Xian People's Hospital, Shangqiu, 476000, China
| | - Zhe Chen
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Xin-Dong Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Kang Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
| | - Pei-Jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Zhou S, Han S, Chen W, Bai X, Pan W, Han X, He X. Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer. Abdom Radiol (NY) 2024; 49:3-10. [PMID: 37787963 DOI: 10.1007/s00261-023-04029-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 10/04/2023]
Abstract
OBJECTIVE Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients. MATERIALS AND METHODS A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801. CONCLUSION Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.
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Affiliation(s)
- Shengnan Zhou
- Department of Gastrointestinal Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Shaoqi Han
- General Surgery Department, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Weijie Chen
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Xuesong Bai
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Weidong Pan
- Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, Beijing, China
| | - Xianlin Han
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
| | - Xiaodong He
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
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Saalfeld S, Kreher R, Hille G, Niemann U, Hinnerichs M, Öcal O, Schütte K, Zech CJ, Loewe C, van Delden O, Vandecaveye V, Verslype C, Gebauer B, Sengel C, Bargellini I, Iezzi R, Berg T, Klümpen HJ, Benckert J, Gasbarrini A, Amthauer H, Sangro B, Malfertheiner P, Preim B, Ricke J, Seidensticker M, Pech M, Surov A. Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients. J Cachexia Sarcopenia Muscle 2023; 14:2301-2309. [PMID: 37592827 PMCID: PMC10570090 DOI: 10.1002/jcsm.13315] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 05/17/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC). METHODS Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups. RESULTS We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134). CONCLUSIONS Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.
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Affiliation(s)
- Sylvia Saalfeld
- Research Campus STIMULATE at the University of MagdeburgMagdeburgGermany
- Department of Simulation and GraphicsUniversity of MagdeburgMagdeburgGermany
| | - Robert Kreher
- Research Campus STIMULATE at the University of MagdeburgMagdeburgGermany
- Department of Simulation and GraphicsUniversity of MagdeburgMagdeburgGermany
| | - Georg Hille
- Research Campus STIMULATE at the University of MagdeburgMagdeburgGermany
- Department of Simulation and GraphicsUniversity of MagdeburgMagdeburgGermany
| | - Uli Niemann
- University LibraryUniversity of MagdeburgMagdeburgGermany
| | - Mattes Hinnerichs
- Department of Radiology and Nuclear MedicineOvGU MagdeburgMagdeburgGermany
| | - Osman Öcal
- Department of RadiologyLMU University HospitalMunichGermany
| | - Kerstin Schütte
- Department of Internal Medicine and GastroenterologyNiels‐Stensen‐Kliniken MarienhospitalOsnabrückGermany
- Klinik für Gastroenterologie, Hepatologie und EndokrinologieMedizinische Hochschule Hannover (MHH)HannoverGermany
| | - Christoph J. Zech
- Department of Radiology and Nuclear MedicineUniversity Hospital Basel, University of BaselBaselSwitzerland
| | - Christian Loewe
- Section of Cardiovascular and Interventional Radiology, Department of Bioimaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Otto van Delden
- Department of Radiology and Nuclear MedicineAcademic University Medical CentersAmsterdamThe Netherlands
| | | | - Chris Verslype
- Department of Digestive OncologyUniversity Hospitals LeuvenLeuvenBelgium
| | - Bernhard Gebauer
- Department of RadiologyCharité – University Medicine BerlinBerlinGermany
| | - Christian Sengel
- Department of RadiologyGrenoble University HospitalLa TroncheFrance
| | - Irene Bargellini
- Diagnostic and Interventional RadiologyCandiolo Cancer InstituteTurinItaly
| | - Roberto Iezzi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Radiologia d'Urgenza e Interventistica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaRomeItaly
- Università Cattolica del Sacro CuoreRomeItaly
| | - Thomas Berg
- Klinik und Poliklinik für Gastroenterologie, Sektion HepatologieUniversitätsklinikum LeipzigLeipzigGermany
| | - Heinz J. Klümpen
- Department of Medical OncologyAmsterdam University Medical CentersAmsterdamThe Netherlands
| | - Julia Benckert
- Department of Hepatology and GastroenterologyCampus Virchow Klinikum, Charité – Universitätsmedizin BerlinBerlinGermany
| | - Antonio Gasbarrini
- Fondazione Policlinico Universitario Gemelli IRCCS, Università Cattolica del Sacro CuoreRomeItaly
| | - Holger Amthauer
- Department of Nuclear MedicineCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Bruno Sangro
- Liver UnitClínica Universidad de Navarra and CIBEREHDPamplonaSpain
| | | | - Bernhard Preim
- Research Campus STIMULATE at the University of MagdeburgMagdeburgGermany
- Department of Simulation and GraphicsUniversity of MagdeburgMagdeburgGermany
| | - Jens Ricke
- Department of RadiologyLMU University HospitalMunichGermany
| | | | - Maciej Pech
- Department of Radiology and Nuclear MedicineOvGU MagdeburgMagdeburgGermany
| | - Alexey Surov
- Department of Radiology, Neuroradiology and Nuclear MedicineJohannes Wesling University Hospital, Ruhr University BochumBochumGermany
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Chen D, Wang W, Wang S, Tan M, Su S, Wu J, Yang J, Li Q, Tang Y, Cao J. Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning. Aging Clin Exp Res 2023; 35:1241-1251. [PMID: 37052817 DOI: 10.1007/s40520-023-02399-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
BACKGROUND Postoperative delirium (POD) is a common and severe complication in elderly hip-arthroplasty patients. AIM This study aims to develop and validate a machine learning (ML) model that determines essential features related to POD and predicts POD for elderly hip-arthroplasty patients. METHODS The electronic record data of elderly patients who received hip-arthroplasty surgery between January 2017 and April 2021 were enrolled as the dataset. The Confusion Assessment Method (CAM) was administered to the patients during their perioperative period. The feature section method was employed as a filter to determine leading features. The classical machine learning algorithms were trained in cross-validation processing, and the model with the best performance was built in predicting the POD. Metrics of the area under the curve (AUC), accuracy (ACC), sensitivity, specificity, and F1-score were calculated to evaluate the predictive performance. RESULTS 476 Arthroplasty elderly patients with general anesthesia were included in this study, and the final model combined feature selection method mutual information (MI) and linear binary classifier using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, specificity = 0.90, F1-score = 0.87) on a balanced test dataset. CONCLUSION The model could predict POD with satisfying accuracy and reveal important features of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count, history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could reduce the incidence of POD among elderly patients.
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Affiliation(s)
- Daiyu Chen
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weijia Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Wang
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Minghe Tan
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Song Su
- Center for Artificial Intelligence in Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jiali Wu
- Center for Artificial Intelligence in Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jun Yang
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qingshu Li
- Department of Pathology, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Jun Cao
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Abenavoli EM, Barbetti M, Linguanti F, Mungai F, Nassi L, Puccini B, Romano I, Sordi B, Santi R, Passeri A, Sciagrà R, Talamonti C, Cistaro A, Vannucchi AM, Berti V. Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques. Cancers (Basel) 2023; 15:cancers15071931. [PMID: 37046592 PMCID: PMC10093023 DOI: 10.3390/cancers15071931] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques. METHODS We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features. RESULTS The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation. CONCLUSIONS Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.
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Affiliation(s)
- Elisabetta Maria Abenavoli
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Matteo Barbetti
- Department of Information Engineering, University of Florence, 50134 Florence, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Florence Division, 50019 Sesto Fiorentino, Italy
| | - Flavia Linguanti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Francesco Mungai
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy
| | - Luca Nassi
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Benedetta Puccini
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Ilaria Romano
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Benedetta Sordi
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Raffaella Santi
- Pathology Section, Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Alessandro Passeri
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Roberto Sciagrà
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Cinzia Talamonti
- Istituto Nazionale di Fisica Nucleare (INFN), Florence Division, 50019 Sesto Fiorentino, Italy
- Medical Physics Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Angelina Cistaro
- Nuclear Medicine Department, Salus Alliance Medical, 16128 Genoa, Italy
- Pediatric Study Group for Italian Association of Nuclear Medicine (AIMN), 20159 Milan, Italy
| | - Alessandro Maria Vannucchi
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Valentina Berti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
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10
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Cannella R, Vernuccio F, Klontzas ME, Ponsiglione A, Petrash E, Ugga L, Pinto dos Santos D, Cuocolo R. Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative. Insights Imaging 2023; 14:21. [PMID: 36720726 PMCID: PMC9889586 DOI: 10.1186/s13244-023-01365-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 12/24/2022] [Indexed: 02/02/2023] Open
Abstract
OBJECTIVES To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies. METHODS A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients. RESULTS A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1-13) for reader 1, 8 (22.2%, IQR 3-12) for reader 2, and 10 (27.8%; IQR 5-14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62-0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1). CONCLUSIONS Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.
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Affiliation(s)
- Roberto Cannella
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy ,grid.10776.370000 0004 1762 5517Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, Via del Vespro, 129, 90127 Palermo, Italy
| | - Federica Vernuccio
- grid.411474.30000 0004 1760 2630Department of Radiology, University Hospital of Padova, Via Nicolò Giustiniani 2, 35128 Padua, Italy
| | - Michail E. Klontzas
- grid.412481.a0000 0004 0576 5678Department of Medical Imaging, University Hospital of Heraklion, 71110 Voutes, Crete, Greece ,grid.8127.c0000 0004 0576 3437Department of Radiology, School of Medicine, University of Crete, 71003 Heraklion, Crete, Greece ,grid.4834.b0000 0004 0635 685XComputational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology, Vassilika Vouton, 70013 Crete, Greece
| | - Andrea Ponsiglione
- grid.4691.a0000 0001 0790 385XDepartment of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Sergio Pansini 5, 80131 Naples, Italy
| | - Ekaterina Petrash
- grid.415738.c0000 0000 9216 2496Radiology Department Research Institute of Children’s Oncology and Hematology, FSBI “National Medical Research Center of Oncology n.a. N.N. Blokhin” of Ministry of Health of RF, Kashirskoye Highway 24, Moscow, Russia ,IRA-Labs, Medical Department, Skolkovo, Bolshoi Boulevard, 30, Building 1, Moscow, Russia
| | - Lorenzo Ugga
- grid.4691.a0000 0001 0790 385XDepartment of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Sergio Pansini 5, 80131 Naples, Italy
| | - Daniel Pinto dos Santos
- grid.6190.e0000 0000 8580 3777Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany ,grid.411088.40000 0004 0578 8220Department of Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Renato Cuocolo
- grid.11780.3f0000 0004 1937 0335Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende 43, 84081 Baronissi, SA Italy ,grid.4691.a0000 0001 0790 385XAugmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, Via Sergio Pansini 5, 80131 Naples, Italy
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11
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Ma Y, Lin Y, Lu J, He Y, Shi Q, Liu H, Li J, Zhang B, Zhang J, Zhang Y, Yue P, Meng W, Li X. A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers. Front Surg 2023; 9:1045295. [PMID: 36684162 PMCID: PMC9852536 DOI: 10.3389/fsurg.2022.1045295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/31/2022] [Indexed: 01/09/2023] Open
Abstract
Background To assess the predictive value of radiomics for preoperative lymph node metastasis (LMN) in patients with biliary tract cancers (BTCs). Methods PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases [VIP, CNKI, Wanfang, and China Biomedical Literature Database (CBM)] were searched to identify relevant studies published up to February 10, 2022. Two authors independently screened all publications for eligibility. We included studies that used histopathology as a gold standard and radiomics to evaluate the diagnostic efficacy of LNM in BTCs patients. The quality of the literature was evaluated using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The diagnostic odds ratio (DOR), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the receiver operating characteristic curve (AUC) were calculated to assess the predictive validity of radiomics for lymph node status in patients with BTCs. Spearman correlation coefficients were calculated, and Meta-regression and subgroup analyses were performed to assess the causes of heterogeneity. Results Seven studies were included, with 977 patients. The pooled sensitivity, specificity and AUC were 83% [95% confidence interval (CI): 77%, 88%], 78% (95% CI: 71, 84) and 0.88 (95% CI: 0.85, 0.90), respectively. The substantive heterogeneity was observed among the included studies (I 2 = 80%, 95%CI: 58,100). There was no threshold effect seen. Meta-regression showed that tumor site contributed to the heterogeneity of specificity analysis (P < 0.05). Imaging methods, number of patients, combined clinical factors, tumor site, model, population, and published year all played a role in the heterogeneity of the sensitivity analysis (P < 0.05). Subgroup analysis revealed that magnetic resonance imaging (MRI) based radiomics had a higher pooled sensitivity than contrast-computed tomography (CT), whereas the result for pooled specificity was the opposite. Conclusion Our meta-analysis showed that radiomics provided a high level of prognostic value for preoperative LMN in BTCs patients.
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Affiliation(s)
- Yuhu Ma
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Yanyan Lin
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Jiyuan Lu
- School of Stomatology, Lanzhou University, Lanzhou, China
| | - Yulong He
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Qianling Shi
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Haoran Liu
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jianlong Li
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Baoping Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jinduo Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Yong Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Ping Yue
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China,Correspondence: Wenbo Meng Ping Yue dryueping@sina. Com
| | - Wenbo Meng
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China,Correspondence: Wenbo Meng Ping Yue dryueping@sina. Com
| | - Xun Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
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12
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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13
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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] [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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14
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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] [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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