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Wiens J, Spector-Bagdady K, Mukherjee B. Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care. Annu Rev Genomics Hum Genet 2024; 25:141-159. [PMID: 38724019 DOI: 10.1146/annurev-genom-010323-010230] [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] [Indexed: 08/29/2024]
Abstract
Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.
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Affiliation(s)
- Jenna Wiens
- Division of Computer Science and Engineering, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA;
| | - Kayte Spector-Bagdady
- Department of Obstetrics and Gynecology and Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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2
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Moro F, Ciancia M, Zace D, Vagni M, Tran HE, Giudice MT, Zoccoli SG, Mascilini F, Ciccarone F, Boldrini L, D'Antonio F, Scambia G, Testa AC. Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review. Int J Cancer 2024. [PMID: 38989809 DOI: 10.1002/ijc.35092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/03/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.
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Affiliation(s)
- Francesca Moro
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Marianna Ciancia
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento di Salute della Donna e del Bambino, Università degli studi di Padova, Padova, Italy
| | - Drieda Zace
- Infectious Disease Clinic, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Marica Vagni
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Huong Elena Tran
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Maria Teresa Giudice
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Sofia Gambigliani Zoccoli
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Medical and Surgical Sciences for Mother, Child and Adult, University of Modena and Reggio Emilia, Azienda Ospedaliero Universitaria Policlinico, Modena, Italy
| | - Floriana Mascilini
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Francesca Ciccarone
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Giovanni Scambia
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Antonia Carla Testa
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
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3
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Di Pilla A, Nero C, Specchia ML, Ciccarone F, Boldrini L, Lenkowicz J, Alberghetti B, Fagotti A, Testa AC, Valentini V, Sala E, Scambia G. A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study). Sci Rep 2024; 14:928. [PMID: 38195911 PMCID: PMC10776619 DOI: 10.1038/s41598-023-51031-1] [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: 06/25/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024] Open
Abstract
Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost-effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18-year-old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA-cancer healthy life expectancy (HLYs). An incremental cost-effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers-healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA-related cancers by 23.7%, generating over a 62-year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA-related cancers (- 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the general population amounts to about 3800€ and 653€ in model 2 and model 3 respectively. Model 2 has a massive effect after only one generation in detecting carriers in the general population with only a small cost increment. The clinical impact is limited mainly due to the current low acceptance rate of risk-reducing surgeries. Further multicentric studies are required before implementing the integrated clinical-radiogenomic model in clinical practice.
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Affiliation(s)
- A Di Pilla
- Dipartimento di Scienze della Vita e Sanità Pubblica - Sezione di Igiene, Università Cattolica del Sacro Cuore, Rome, Italy
| | - C Nero
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - M L Specchia
- Dipartimento di Scienze della Vita e Sanità Pubblica - Sezione di Igiene, Università Cattolica del Sacro Cuore, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
| | - F Ciccarone
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - L Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - J Lenkowicz
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - B Alberghetti
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - A Fagotti
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - A C Testa
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - V Valentini
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - E Sala
- Università Cattolica del Sacro Cuore, Rome, Italy
- UOC Radiologia, Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - G Scambia
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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4
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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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5
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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6
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Moro F, Boldrini L, Lenkowicz J, Scambia G, Testa AC, Fanfani F. Reply. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:299-300. [PMID: 35913380 DOI: 10.1002/uog.24963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Affiliation(s)
- F Moro
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - L Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Radioterapia Oncologica, Rome, Italy
| | - J Lenkowicz
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC Radioterapia Oncologica, Rome, Italy
| | - G Scambia
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Istituto di Clinica Ostetrica e Ginecologica, Rome, Italy
| | - A C Testa
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Istituto di Clinica Ostetrica e Ginecologica, Rome, Italy
| | - F Fanfani
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Istituto di Clinica Ostetrica e Ginecologica, Rome, Italy
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Zhang L, Zhang B. Ultrasound-based radiomics features: a gain or loss for risk stratification in patients with endometrial cancer. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:298-299. [PMID: 35913381 DOI: 10.1002/uog.24962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/23/2021] [Indexed: 05/27/2023]
Affiliation(s)
- L Zhang
- Department of Radiology, the First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - B Zhang
- Department of Radiology, the First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
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8
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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] [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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9
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Liu J, Zhao H, Zheng Y, Dong L, Zhao S, Huang Y, Huang S, Qian T, Zou J, Liu S, Li J, Yan Z, Li Y, Zhang S, Huang X, Wang W, Li Y, Wang J, Ming Y, Li X, Xing Z, Qin L, Zhao Z, Jia Z, Li J, Liu G, Zhang M, Feng K, Wu J, Zhang J, Yang Y, Wu Z, Liu Z, Ying J, Wang X, Su J, Wang X, Wu N. DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data. Genome Med 2022; 14:21. [PMID: 35209950 PMCID: PMC8876403 DOI: 10.1186/s13073-022-01027-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/10/2022] [Indexed: 11/10/2022] Open
Abstract
Background Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genetic testing among breast cancer patients do not meet the demands of clinical practice due to insufficient accuracy. Methods The study population comprised 3041 breast cancer patients enrolled from seven hospitals between October 2017 and 11 August 2019, who underwent germline genetic testing of 50 cancer predisposition genes (CPGs). Associations among GPVs in different CPGs and endophenotypes were evaluated using a case-control analysis. A phenotype-based GPV risk prediction model named DNA-repair Associated Breast Cancer (DrABC) was developed based on hierarchical neural network architecture and validated in an independent multicenter cohort. The predictive performance of DrABC was compared with currently used models including BRCAPRO, BOADICEA, Myriad, PENN II, and the NCCN criteria. Results In total, 332 (11.3%) patients harbored GPVs in CPGs, including 134 (4.6%) in BRCA2, 131 (4.5%) in BRCA1, 33 (1.1%) in PALB2, and 37 (1.3%) in other CPGs. GPVs in CPGs were associated with distinct endophenotypes including the age at diagnosis, cancer history, family cancer history, and pathological characteristics. We developed a DrABC model to predict the risk of GPV carrier status in BRCA1/2 and other important CPGs. In predicting GPVs in BRCA1/2, the performance of DrABC (AUC = 0.79 [95% CI, 0.74–0.85], sensitivity = 82.1%, specificity = 63.1% in the independent validation cohort) was better than that of previous models (AUC range = 0.57–0.70). In predicting GPVs in any CPG, DrABC (AUC = 0.74 [95% CI, 0.69–0.79], sensitivity = 83.8%, specificity = 51.3% in the independent validation cohort) was also superior to previous models in their current versions (AUC range = 0.55–0.65). After training these previous models with the Chinese-specific dataset, DrABC still outperformed all other methods except for BOADICEA, which was the only previous model with the inclusion of pathological features. The DrABC model also showed higher sensitivity and specificity than the NCCN criteria in the multi-center validation cohort (83.8% and 51.3% vs. 78.8% and 31.2%, respectively, in predicting GPVs in any CPG). The DrABC model implementation is available online at http://gifts.bio-data.cn/. Conclusions By considering the distinct endophenotypes associated with different CPGs in breast cancer patients, a phenotype-driven prediction model based on hierarchical neural network architecture was created for identification of hereditary breast cancer. The model achieved superior performance in identifying GPV carriers among Chinese breast cancer patients. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01027-9.
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Affiliation(s)
- Jiaqi Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.,Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, 325027, China
| | - Hengqiang Zhao
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yu Zheng
- Fintech Innovation Center, Southwestern University of Finance and Economics, Chengdu, 611130, China
| | - Lin Dong
- Department of Pathology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sen Zhao
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, 325027, China.,School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Shengkai Huang
- Department of Laboratory Medicine, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Tianyi Qian
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiali Zou
- Department of Breast Surgery, Guiyang Maternal and Child Healthcare Hospital, Guiyang, 550001, China
| | - Shu Liu
- Department of Breast Surgery, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China
| | - Jun Li
- Department of Molecular Pathology, the Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, 450000, China
| | - Zihui Yan
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yalun Li
- Department of Breast Surgery, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, China
| | - Shuo Zhang
- Department of Breast Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050019, Hebei, China
| | - Xin Huang
- Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Wenyan Wang
- Department of Breast Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Yiqun Li
- Department of Oncology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jie Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yue Ming
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoxin Li
- Medical Research Center, Beijing Key Laboratory for Genetic Research of Skeletal Deformity & Key Laboratory of Big Data for Spinal Deformities, All at Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Zeyu Xing
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ling Qin
- Department of Breast Surgical Oncology, Cancer Hospital of HuanXing, Beijing, 100021, China
| | - Zhengye Zhao
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ziqi Jia
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiaxin Li
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Gang Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Menglu Zhang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Kexin Feng
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiang Wu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianguo Zhang
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Key Laboratory of Big Data for Spinal Deformities, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,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, 100730, China
| | - Yongxin Yang
- Machine Intelligence Group, University of Edinburgh, Edinburgh, EH8 9YL, UK
| | - Zhihong Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Medical Research Center, Beijing Key Laboratory for Genetic Research of Skeletal Deformity & Key Laboratory of Big Data for Spinal Deformities, All at Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,Key Laboratory of Big Data for Spinal Deformities, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.,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, 100730, China
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianming Ying
- Department of Pathology, National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xin Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, 325027, China. .,School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China. .,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China.
| | - Xiang Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Nan Wu
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. .,Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. .,Key Laboratory of Big Data for Spinal Deformities, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. .,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, 100730, China.
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10
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Froelich MF, Capoluongo E, Kovacs Z, Patton SJ, Lianidou ES, Haselmann V. The value proposition of integrative diagnostics for (early) detection of cancer. On behalf of the EFLM interdisciplinary Task and Finish Group "CNAPS/CTC for early detection of cancer". Clin Chem Lab Med 2022; 60:821-829. [PMID: 35218176 DOI: 10.1515/cclm-2022-0129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/12/2022]
Abstract
Disruptive imaging and laboratory technologies can improve clinical decision processes and outcomes in oncology. However, certain obstacles must be overcome before these technologies can be fully implemented as part of the standard for care. An integrative diagnostic approach represents a unique opportunity to unleash the full diagnostic potential and paves the way towards personalized cancer diagnostics. To meet this demand, an interdisciplinary Task Force of the EFLM was initiated as a consequence of an EFLM/ESR during the CELME 2019 meeting in order to evaluate the clinical value of CNAPS/CTC (circulating nucleic acids in plasma and serum/circulating tumor cells) in early detection of cancer. Here, an overview of current disruptive techniques, their clinical implications and potential value of an integrative diagnostic approach is provided. Furthermore, requirements such as the establishment of diagnostic tumor boards, development of adequate software solutions and a change of mindset towards a new generation of diagnosticians providing actionable health information are presented. This development has the potential to elevate the position and clinical recognition of diagnosticians.
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Affiliation(s)
- Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medicine Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Mannheim, Germany
| | - Ettore Capoluongo
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Naples, Italy
- CEINGE-Biotecnologie Avanzate, Napoli, Italy
| | - Zsolt Kovacs
- Department of Pathology, Clinical County Emergency Hospital, Targu-Mures, Romania
| | | | - Evi S Lianidou
- Department of Chemistry, Analysis of Circulating Tumor Cells Lab, Laboratory of Analytical Chemistry, University of Athens, Athens, Greece
| | - Verena Haselmann
- Medical Faculty Mannheim of the University of Heidelberg, Institute of Clinical Chemistry, University Hospital Mannheim, Mannheim, Germany
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11
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Song H, Bak S, Kim I, Woo JY, Cho EJ, Choi YJ, Rha SE, Oh SA, Youn SY, Lee SJ. An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer. J Clin Med 2021; 11:jcm11010229. [PMID: 35011970 PMCID: PMC8745699 DOI: 10.3390/jcm11010229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/13/2022] Open
Abstract
This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADCmean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.
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Affiliation(s)
- Heekyoung Song
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Seongeun Bak
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Imhyeon Kim
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Jae Yeon Woo
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Eui Jin Cho
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Youn Jin Choi
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
| | - Shin Ah Oh
- NAVER Clova, 246, Hwangsaeul-ro, Bundang-gu, Seongnam-si 13595, Korea;
| | - Seo Yeon Youn
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
- Correspondence: (S.Y.Y.); (S.J.L.)
| | - Sung Jong Lee
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
- Correspondence: (S.Y.Y.); (S.J.L.)
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12
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Damiani A, Masciocchi C, Lenkowicz J, Capocchiano ND, Boldrini L, Tagliaferri L, Cesario A, Sergi P, Marchetti A, Luraschi A, Patarnello S, Valentini V. Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.768266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.
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13
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Moro F, Bertoldo V, Avesani G, Moruzzi MC, Mascilini F, Bolomini G, Caliolo G, Esposito R, Moroni R, Zannoni GF, Fagotti A, Manfredi R, Scambia G, Testa AC. Fusion imaging in preoperative assessment of extent of disease in patients with advanced ovarian cancer: feasibility and agreement with laparoscopic findings. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2021; 60:256-268. [PMID: 33847427 DOI: 10.1002/uog.24805] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 10/07/2021] [Accepted: 10/13/2021] [Indexed: 05/25/2023]
Abstract
OBJECTIVES Fusion imaging is an emerging technique that combines real-time ultrasound examination with images acquired previously using other modalities, such as computed tomography (CT), magnetic resonance imaging and positron emission tomography. The primary aim of this study was to evaluate the feasibility of fusion imaging in patients with suspicion of ovarian or peritoneal cancer. Secondary aims were: to compare the agreement of findings on fusion imaging, CT alone and ultrasound imaging alone with laparoscopic findings, in the assessment of extent of intra-abdominal disease; and to evaluate the time required for the fusion imaging technique. METHODS Patients with clinical and/or radiographic suspicion of advanced ovarian or peritoneal cancer who were candidates for surgery were enrolled prospectively between December 2019 and September 2020. All patients underwent a CT scan and ultrasound and fusion imaging to evaluate the presence or absence of the following abdominal-cancer features according to the laparoscopy-based scoring model (predictive index value (PIV)): supracolic omental disease, visceral carcinomatosis on the liver, lesser omental carcinomatosis and/or visceral carcinomatosis on the lesser curvature of the stomach and/or spleen, involvement of the paracolic gutter(s) and/or anterior abdominal wall, involvement of the diaphragm and visceral carcinomatosis on the small and/or large bowel (regardless of rectosigmoid involvement). The feasibility of the fusion examination in these patients was evaluated. Agreement of each imaging method (ultrasound, CT and fusion imaging) with laparoscopy (considered as reference standard) was calculated using Cohen's kappa coefficient. RESULTS Fifty-two patients were enrolled into the study. Fusion imaging was feasible in 51 (98%) of these patients (in one patient, it was not possible for technical reasons). Two patients were excluded because laparoscopy was not performed, leaving 49 women in the final analysis. Kappa values for CT, ultrasound and fusion imaging, using laparoscopy as the reference standard, in assessing the PIV parameters were, respectively: 0.781, 0.845 and 0.896 for the great omentum; 0.329, 0.608 and 0.847 for the liver surface; 0.472, 0.549 and 0.756 for the lesser omentum and/or stomach and/or spleen; 0.385, 0.588 and 0.795 for the paracolic gutter(s) and/or anterior abdominal wall; 0.385, 0.497 and 0.657 for the diaphragm; and 0.336, 0.410 and 0.469 for the bowel. The median time needed to perform the fusion examination was 20 (range, 10-40) min. CONCLUSION Fusion of CT images and real-time ultrasound imaging is feasible in patients with suspicion of ovarian or peritoneal cancer and improves the agreement with surgical findings when compared with ultrasound or CT scan alone. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- F Moro
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - V Bertoldo
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Avesani
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
| | - M C Moruzzi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - F Mascilini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Bolomini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Caliolo
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
| | - R Esposito
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - R Moroni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Direzione Scientifica, Rome, Italy
| | - G F Zannoni
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Istituto di Anatomia Patologica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - A Fagotti
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
| | - R Manfredi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - G Scambia
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
| | - A C Testa
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
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14
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Di Giannatale A, Di Paolo PL, Curione D, Lenkowicz J, Napolitano A, Secinaro A, Tomà P, Locatelli F, Castellano A, Boldrini L. Radiogenomics prediction for MYCN amplification in neuroblastoma: A hypothesis generating study. Pediatr Blood Cancer 2021; 68:e29110. [PMID: 34003574 DOI: 10.1002/pbc.29110] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/13/2021] [Accepted: 04/23/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information. PROCEDURE In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status. NB lesions were segmented on pretherapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome. RESULTS Seventy-eight patients were included in this study, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p = .0082) and zone size non-uniformity (p = .038). Five-times repeated three-fold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data. CONCLUSIONS CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.
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Affiliation(s)
- Angela Di Giannatale
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | | | - Davide Curione
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Aurelio Secinaro
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Paolo Tomà
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Franco Locatelli
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy.,Department of Gynecology/Obstetrics and Pediatrics, Sapienza University of Rome, Rome, Italy
| | - Aurora Castellano
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
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15
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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16
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Horgan D, Ciliberto G, Conte P, Curigliano G, Seijo L, Montuenga LM, Garassino M, Penault-Llorca F, Galli F, Ray-Coquard I, Querleu D, Riegman P, Kerr K, Van Poppel H, Bjartell A, Codacci-Pisanelli G, Koeva-Balabanova J, Paradiso A, Maravic Z, Fotaki V, Malats N, Bernini C, Buglioni S, Kent A, Munzone E, Belina I, Van Meerbeeck J, Duffy M, Jagielska B, Capoluongo E. Bringing Onco-Innovation to Europe's Healthcare Systems: The Potential of Biomarker Testing, Real World Evidence, Tumour Agnostic Therapies to Empower Personalised Medicine. Cancers (Basel) 2021; 13:cancers13030583. [PMID: 33540773 PMCID: PMC7867284 DOI: 10.3390/cancers13030583] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/18/2020] [Accepted: 01/15/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The increasing number of data supporting use of a personalized approach in cancer treatment, is changing the path of patient’s management. In the same time, the availability of technologies should allow patients to receive the best test for the specific individual condition. This is theoretically true, when a specific test is designed for the specific disease condition, while it is difficult to implement in the setting of agnostic therapies. Financial sources availability related to the non homogeneous health systems working in the different countries do not allow for an immediate implementation of the technologies and test commercially available. Future perspectives for targeted oncology include tumor-agnostic drugs, which target a given mutation and could be used in treating cancers from multiple organ types. Therefore, the present paper is aimed to both underline a how much important is this new view and also to sensitize the international bodies that supervise health policies at the decision-making level, with the aim of harmonizing cancer treatment pathways in at least all European countries. Abstract Rapid and continuing advances in biomarker testing are not being matched by uptake in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. The potential that genomics has brought to biomarker testing in diagnosis, prediction and research is being realised, pre-eminently in many cancers, but also in an ever-wider range of conditions—notably BRCA1/2 testing in ovarian, breast, pancreatic and prostate cancers. Nevertheless, the implementation of genetic testing in clinical routine setting is still challenging. Development is impeded by country-related heterogeneity, data deficiencies, and lack of policy alignment on standards, approval—and the role of real-world evidence in the process—and reimbursement. The acute nature of the problem is compellingly illustrated by the particular challenges facing the development and use of tumour agnostic therapies, where the gaps in preparedness for taking advantage of this innovative approach to cancer therapy are sharply exposed. Europe should already have in place a guarantee of universal access to a minimum suite of biomarker tests and should be planning for an optimum testing scenario with a wider range of biomarker tests integrated into a more sophisticated health system articulated around personalised medicine. Improving healthcare and winning advantages for Europe’s industrial competitiveness and innovation require an appropriate policy framework—starting with an update to outdated recommendations. We show herein the main issues and proposals that emerged during the previous advisory boards organised by the European Alliance for Personalized Medicine which mainly focus on possible scenarios of harmonisation of both oncogenetic testing and management of cancer patients.
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Affiliation(s)
- Denis Horgan
- European Alliance for Personalized Medicine, Avenue de l’Armee/ Legerlaan 10, 1040 Brussels, Belgium;
- Correspondence: (D.H.); (E.C.)
| | - Gennaro Ciliberto
- IRCCS Istituto Nazionale Tumori “Regina Elena”, Via Elio Chianesi, 53, 00128 Rome, Italy; (G.C.); (S.B.)
| | - Pierfranco Conte
- Dipartimento di Scienze Chirurgiche Oncologiche e Gastroenterologiche, University of Padova, Via Giustiniani 2, 35128 Padova, Italy;
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milano and European Institute of Oncology, IRCCS, 20139 Milano, Italy;
| | - Luis Seijo
- Pulmonary Department, Clínica Universidad de Navarra, Calle Marquesado de Sta. Marta, 1, 28027 Madrid, Spain;
- Ciber Enfermedades Respiratorias (CIBERES), Av. de Monforte de Lemos, 3-5, 28029 Madrid, Spain
| | - Luis M. Montuenga
- Center for Applied Medical Research (CIMA), Schools of Sciences and Medicine, University of Navarra, Av. de Pío XII, 55, 31008 Pamplona, Spain;
- CIBERONC, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain;
| | - Marina Garassino
- S.S. Oncologia Medica Toraco Polmonare, Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian, 1, 20133 Milano, Italy;
| | - Frederique Penault-Llorca
- Department of Pathology and Tumor Biology, University of Clermont-Auvergne, 49 bd François Mitterrand, 63001 Clermont-Ferrand, France;
| | - Fabrizia Galli
- Associazione aBRCAdaBRA Onlus Via Volontari Italiani del Sangue, 32, 90128 Palermo, Italy;
| | - Isabelle Ray-Coquard
- Medical Oncology Department, Centre Leon Bérard & Université Claude Bernard Lyon, 69008 Lyon, France;
| | - Denis Querleu
- Surgery Department, Institut Bergonié Cancer Center, Centre Léon Bérard Cheney D- 2 ème étage -28 Rue Laennec, 69373 Lyon, France;
| | - Peter Riegman
- Department of Pathology, Josephine Nefkens Institute, Erasmus Medical Center, Be 235b, Dr Molwaterplein 50, 3015 Rotterdam, The Netherlands;
| | - Keith Kerr
- Department of Pathology, University of Aberdeen, King’s College, Aberdeen AB24 3FX, UK;
| | - Hein Van Poppel
- Department of Urology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium;
| | - Anders Bjartell
- Department of Urology, Skane University Hospital, Box 117, 221 00 Lund, Sweden;
| | - Giovanni Codacci-Pisanelli
- Department of Medical and Surgical Sciences and Biotechnology, University of Rome, “la Sapienza”, Piazzale Aldo Moro, 5, 00185 Roma, Italy;
| | | | - Angelo Paradiso
- Scientific Directorate, IRCCS Istituto Tumori Giovanni Paolo II, Viale Orazio Flacco, 65, 70124 Bari, Italy;
| | - Zorana Maravic
- Digestive Cancers Europe, Rue de la Loi 235, 1040 Brussels, Belgium; (Z.M.); (V.F.)
| | - Vassiliki Fotaki
- Digestive Cancers Europe, Rue de la Loi 235, 1040 Brussels, Belgium; (Z.M.); (V.F.)
| | - Nuria Malats
- CIBERONC, Av. Monforte de Lemos, 3-5, 28029 Madrid, Spain;
- Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernández Almagro, 3, 28029 Madrid, Spain
| | - Chiara Bernini
- European Alliance for Personalized Medicine, Avenue de l’Armee/ Legerlaan 10, 1040 Brussels, Belgium;
| | - Simonetta Buglioni
- IRCCS Istituto Nazionale Tumori “Regina Elena”, Via Elio Chianesi, 53, 00128 Rome, Italy; (G.C.); (S.B.)
| | - Alastair Kent
- Independent Patient Advocate, 14 Farthing Road Downham Market, Norfolk PE38 0AF, UK;
| | - Elisabetta Munzone
- Division of Medical Senology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milano, Italy;
| | - Ivica Belina
- KUZ-Coalition of Association in Healthcare, Trpimirova 11, 10000 Zagreb, Croatia;
| | - Jan Van Meerbeeck
- Thoracic Oncology-MOCA, University Hospital Antwerp, Wilrijkstraat 10, 2650 Edegem, Belgium;
| | - Michael Duffy
- UCD School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland;
| | - Beata Jagielska
- Maria Skłodowska-Curie Institute of Oncology, Wawelska 15 B, 00-001 Warszawa, Poland;
| | - Ettore Capoluongo
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, 80131 Naples, Italy
- CEINGE-Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80131 Napoli, Italy
- Correspondence: (D.H.); (E.C.)
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