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Yu Y, Wang Z, Wang Q, Su X, Li Z, Wang R, Guo T, Gao W, Wang H, Zhang B. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Front Oncol 2024; 13:1249339. [PMID: 38357424 PMCID: PMC10865896 DOI: 10.3389/fonc.2023.1249339] [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: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/16/2024] Open
Abstract
Purpose To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. Method MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. Results The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. Conclusion We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.
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Affiliation(s)
- Yimiao Yu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhibo Wang
- Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qi Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaohui Su
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhenghao Li
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruifeng Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianhui Guo
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen Gao
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiji Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Biyuan Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Rahimi M, Akbari A, Asadi F, Emami H. Cervical cancer survival prediction by machine learning algorithms: a systematic review. BMC Cancer 2023; 23:341. [PMID: 37055741 PMCID: PMC10103471 DOI: 10.1186/s12885-023-10808-3] [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: 02/06/2023] [Accepted: 04/05/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Cervical cancer is a common malignant tumor of the female reproductive system and is considered a leading cause of mortality in women worldwide. The analysis of time to event, which is crucial for any clinical research, can be well done with the method of survival prediction. This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer. METHOD An electronic search of the PubMed, Scopus, and Web of Science databases was performed on October 1, 2022. All articles extracted from the databases were collected in an Excel file and duplicate articles were removed. The articles were screened twice based on the title and the abstract and checked again with the inclusion and exclusion criteria. The main inclusion criterion was machine learning algorithms for predicting cervical cancer survival. The information extracted from the articles included authors, publication year, dataset details, survival type, evaluation criteria, machine learning models, and the algorithm execution method. RESULTS A total of 13 articles were included in this study, most of which were published from 2018 onwards. The most common machine learning models were random forest (6 articles, 46%), logistic regression (4 articles, 30%), support vector machines (3 articles, 23%), ensemble and hybrid learning (3 articles, 23%), and Deep Learning (3 articles, 23%). The number of sample datasets in the study varied between 85 and 14946 patients, and the models were internally validated except for two articles. The area under the curve (AUC) range for overall survival (0.40 to 0.99), disease-free survival (0.56 to 0.88), and progression-free survival (0.67 to 0.81), respectively from (lowest to highest) received. Finally, 15 variables with an effective role in predicting cervical cancer survival were identified. CONCLUSION Combining heterogeneous multidimensional data with machine learning techniques can play a very influential role in predicting cervical cancer survival. Despite the benefits of machine learning, the problem of interpretability, explainability, and imbalanced datasets is still one of the biggest challenges. Providing machine learning algorithms for survival prediction as a standard requires further studies.
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Affiliation(s)
- Milad Rahimi
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Atieh Akbari
- Obstetrics and Gynecology, Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, Health Information Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hassan Emami
- Department of Health Information Technology and Management, Information Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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La Forgia D, Paparella G, Signorile R, Arezzo F, Comes MC, Cormio G, Daniele A, Fanizzi A, Fioretti AM, Gatta G, Lafranceschina M, Rizzo A, Zaccaria GM, Rosa A, Massafra R. Lean Perspectives in an Organizational Change in a Scientific Direction of an Italian Research Institute: Experience of the Cancer Institute of Bari. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:239. [PMID: 36612562 PMCID: PMC9819426 DOI: 10.3390/ijerph20010239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Lean management is a relatively new organizational vision transferred from the automotive industry to the healthcare and administrative sector based on analyzing a production process to emphasize value and reduce waste. This approach is particularly interesting in a historical moment of cuts and scarcity of economic resources and could represent a low-cost organizational solution in many production companies. In this work, we analyzed the presentation and the initial management of current ministerial research projects up to the approval by the Scientific Directorate of an Italian research institute. Furthermore, the initial mode in 2021 ("as is") and the potential mode ("to be") according to a Lean model are studied, according to the current barriers highlighted by the final users of the process and carrying out some perspective analyses with some reference indicators.
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Affiliation(s)
- Daniele La Forgia
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gaetano Paparella
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Rahel Signorile
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Francesca Arezzo
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gennaro Cormio
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Department of Interdisciplinary Medicine, University of Bari, 70124 Bari, Italy
| | - Antonella Daniele
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Agnese Maria Fioretti
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Miria Lafranceschina
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Alessandro Rizzo
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gian Maria Zaccaria
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Angelo Rosa
- Department of Management, Finance and Technology, LUM University, 70010 Casamassima, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
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Arezzo F, Cormio G, La Forgia D, Santarsiero CM, Mongelli M, Lombardi C, Cazzato G, Cicinelli E, Loizzi V. A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients. Arch Gynecol Obstet 2022; 306:2143-2154. [PMID: 35532797 PMCID: PMC9633520 DOI: 10.1007/s00404-022-06578-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023]
Abstract
In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.
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Affiliation(s)
- Francesca Arezzo
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gennaro Cormio
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Daniele La Forgia
- Department of Breast Radiology, Giovanni Paolo II I.R.C.C.S. Cancer Institute, via Orazio Flacco 65, 70124 Bari, Italy
| | - Carla Mariaflavia Santarsiero
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Michele Mongelli
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Claudio Lombardi
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gerardo Cazzato
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Ettore Cicinelli
- Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Vera Loizzi
- Interdisciplinar Department of Medicine, Obstetrics and Gynecology Unit, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
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Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy. Arch Gynecol Obstet 2022; 307:1911-1919. [PMID: 36370209 DOI: 10.1007/s00404-022-06824-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore, the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients. METHODS We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with tenfold cross-validation. The performances of the algorithm were assessed. RESULTS Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre- and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79). CONCLUSIONS We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.
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Abdalvand N, Sadeghi M, Mahdavi SR, Abdollahi H, Qasempour Y, Mohammadian F, Birgani MJT, Hosseini K. Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters. Brachytherapy 2022; 21:769-782. [PMID: 35933272 DOI: 10.1016/j.brachy.2022.06.007] [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: 03/12/2022] [Revised: 06/09/2022] [Accepted: 06/26/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. METHODS Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. RESULTS One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). CONCLUSION Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.
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Affiliation(s)
- Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Sadeghi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Younes Qasempour
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Fatemeh Mohammadian
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | | | - Khadijeh Hosseini
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
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Bautista-Valarezo E, Vega Crespo B, Maldonado-Rengel R, Espinosa ME, Neira VA, Verhoeven V. Knowledge and Perceptions about Cervical Cancer and HPV Screening in Women in Rural Areas of Ecuador: A Qualitative Research Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11053. [PMID: 36078764 PMCID: PMC9517721 DOI: 10.3390/ijerph191711053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Cervical cancer continues to be a major health problem in developing countries. Educational programs, as well as Pap and HPV screening and vaccination, are important tools to reduce the morbidity and mortality rates associated with this disease. The objective of this study is to explore the diverse knowledge and perceptions about cervical cancer and the different diagnostic tests for HPV of populations living in the rural parish "El Valle". METHOD A qualitative study was conducted through eight focus groups, which included 46 participants from mixed ethnic groups. A phenomenological analysis was performed. RESULTS Four topics and seven sub-topics were identified. By analyzing all the narratives, it was possible to identify that the perception of cervical cancer was focused on its severity, secondary to its infectious process and screening periodicity. However, despite the diverse knowledge, indigenous people do not relate it to the human papilloma virus; in addition, there is also certain resistance to undergo the Pap smear test, for reasons such as inaccessibility and its sampling process. CONCLUSIONS It is necessary to develop educational programs for the prevention of cervical cancer and to implement diagnostic alternatives to reach populations with precarious accessibility, as well as women who refuse to undergo the Pap smear test.
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Affiliation(s)
- Estefanía Bautista-Valarezo
- Facultad de Ciencias de la Salud, Universidad Técnica Particular de Loja, Loja 1101608, Ecuador; (R.M.-R.); (M.E.E.)
| | - Bernardo Vega Crespo
- Facultad de Ciencias Médicas, Universidad de Cuenca, Cuenca 010203, Ecuador; (B.V.C.); (V.A.N.)
| | - Ruth Maldonado-Rengel
- Facultad de Ciencias de la Salud, Universidad Técnica Particular de Loja, Loja 1101608, Ecuador; (R.M.-R.); (M.E.E.)
- Programa de Doctorado en Ciencias Morfológicas, Universidad de la Frontera, Temuco 4811230, Chile
| | - María Elena Espinosa
- Facultad de Ciencias de la Salud, Universidad Técnica Particular de Loja, Loja 1101608, Ecuador; (R.M.-R.); (M.E.E.)
- Programa de Doctorado en Ciencias Médicas, Universidad de la Frontera, Temucho 4811230, Chile
| | - Vivian Alejandra Neira
- Facultad de Ciencias Médicas, Universidad de Cuenca, Cuenca 010203, Ecuador; (B.V.C.); (V.A.N.)
| | - Veronique Verhoeven
- Department of Family Medicine and Population Health, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium;
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Venerito V, Emmi G, Cantarini L, Leccese P, Fornaro M, Fabiani C, Lascaro N, Coladonato L, Mattioli I, Righetti G, Malandrino D, Tangaro S, Palermo A, Urban ML, Conticini E, Frediani B, Iannone F, Lopalco G. Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering. Front Immunol 2022; 13:860877. [PMID: 35450069 PMCID: PMC9017227 DOI: 10.3389/fimmu.2022.860877] [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: 01/23/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Inferential statistical methods failed in identifying reliable biomarkers and risk factors for relapsing giant cell arteritis (GCA) after glucocorticoids (GCs) tapering. A ML approach allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome. Objective The objective of the study was to assess whether ML algorithms can predict GCA relapse after GCs tapering. Methods GCA patients who underwent GCs therapy and regular follow-up visits for at least 12 months, were retrospectively analyzed and used for implementing 3 ML algorithms, namely, Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The outcome of interest was disease relapse within 3 months during GCs tapering. After a ML variable selection method, based on a XGBoost wrapper, an attribute core set was used to train and test each algorithm using 5-fold cross-validation. The performance of each algorithm in both phases was assessed in terms of accuracy and area under receiver operating characteristic curve (AUROC). Results The dataset consisted of 107 GCA patients (73 women, 68.2%) with mean age ( ± SD) 74.1 ( ± 8.5) years at presentation. GCA flare occurred in 40/107 patients (37.4%) within 3 months after GCs tapering. As a result of ML wrapper, the attribute core set with the least number of variables used for algorithm training included presence/absence of diabetes mellitus and concomitant polymyalgia rheumatica as well as erythrocyte sedimentation rate level at GCs baseline. RF showed the best performance, being significantly superior to other algorithms in accuracy (RF 71.4% vs LR 70.4% vs DT 62.9%). Consistently, RF precision (72.1%) was significantly greater than those of LR (62.6%) and DT (50.8%). Conversely, LR was superior to RF and DT in recall (RF 60% vs LR 62.5% vs DT 47.5%). Moreover, RF AUROC (0.76) was more significant compared to LR (0.73) and DT (0.65). Conclusions RF algorithm can predict GCA relapse after GCs tapering with sufficient accuracy. To date, this is one of the most accurate predictive modelings for such outcome. This ML method represents a reproducible tool, capable of supporting clinicians in GCA patient management.
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Affiliation(s)
- Vincenzo Venerito
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Giacomo Emmi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Luca Cantarini
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Pietro Leccese
- Rheumatology Department of Lucania, San Carlo Hospital of Potenza, Potenza, Italy
| | - Marco Fornaro
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Claudia Fabiani
- Ophthalmology Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nancy Lascaro
- Rheumatology Department of Lucania, San Carlo Hospital of Potenza, Potenza, Italy
| | - Laura Coladonato
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Irene Mattioli
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giulia Righetti
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Danilo Malandrino
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, University of Bari "Aldo Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Adalgisa Palermo
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Maria Letizia Urban
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Edoardo Conticini
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Bruno Frediani
- Research Centre of Systemic Autoinflammatory Diseases, Behçet's Disease Clinic and Rheumatology-Ophthalmology Collaborative Uveitis Centre, Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - Florenzo Iannone
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
| | - Giuseppe Lopalco
- Department of Emergency and Organ Transplantation, Rheumatology Unit, University of Bari, Bari, Italy
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Arezzo F, Loizzi V, La Forgia D, Abdulwakil Kawosha A, Silvestris E, Cataldo V, Lombardi C, Cazzato G, Ingravallo G, Resta L, Cormio G. The Role of Ultrasound Guided Sampling Procedures in the Diagnosis of Pelvic Masses: A Narrative Review of the Literature. Diagnostics (Basel) 2021; 11:diagnostics11122204. [PMID: 34943440 PMCID: PMC8699999 DOI: 10.3390/diagnostics11122204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 02/05/2023] Open
Abstract
Ultrasound-guided sampling methods are usually minimally invasive techniques applied to obtain cytological specimens or tissue samples, mainly used for the diagnosis of different types of tumors. The main benefits of ultrasound guidance is its availability. It offers high flexibility in the choice of sampling approach (transabdominal, transvaginal, and transrectal) and short duration of procedure. Ultrasound guided sampling of pelvic masses represents the diagnostic method of choice in selected patients. We carried out a narrative review of literatures regarding the ultrasound-guided methods of cytological and histological evaluation of pelvic masses as well as the positive and negative predictors for the achievement of an adequate sample.
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Affiliation(s)
- Francesca Arezzo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (V.C.); (C.L.); (G.C.)
- Correspondence: ; Tel.: +39-3274961788
| | - Vera Loizzi
- Obstetrics and Gynecology Unit, Interdisciplinar Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy;
| | - Daniele La Forgia
- SSD Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II”, Via Orazio Flacco 65, 70124 Bari, Italy;
| | - Adam Abdulwakil Kawosha
- Department of General Medicine, Universitatea Medicina si Farmacie Grigore T Popa, Strada Universitatii 16, 700115 Iasi, Romania;
| | - Erica Silvestris
- Gynecologic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II”, Via Orazio Flacco 65, 70124 Bari, Italy;
| | - Viviana Cataldo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (V.C.); (C.L.); (G.C.)
| | - Claudio Lombardi
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (V.C.); (C.L.); (G.C.)
| | - Gerardo Cazzato
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (G.I.); (L.R.)
| | - Giuseppe Ingravallo
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (G.I.); (L.R.)
| | - Leonardo Resta
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (G.I.); (L.R.)
| | - Gennaro Cormio
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (V.C.); (C.L.); (G.C.)
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Arezzo F, Cormio G, Loizzi V, Cazzato G, Cataldo V, Lombardi C, Ingravallo G, Resta L, Cicinelli E. HPV-Negative Cervical Cancer: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060952. [PMID: 34073478 PMCID: PMC8229781 DOI: 10.3390/diagnostics11060952] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 02/05/2023] Open
Abstract
Cervical cancer (CC) is the fourth most frequent cancer in women worldwide. HPV infection is associated with the majority of CC cases, but a small proportion of CCs actually test negative for HPV. The prevalence of HPV among CC histotypes is very different. It has been suggested that HPV-negative CC may represent a biologically distinct subset of tumors, relying on a distinct pathogenetic pathway and carrying a poorer prognosis, than HPV-positive CCs. Although, the discordance in terms of sensitivity and specificity between different HPV tests as well as the potential errors in sampling and storing tissues may be considered as causes of false-negative results. The identification of HPV-negative CCs is essential for their correct management. The aim of this narrative review is to summarize the clinical and pathological features of this variant. We also discuss the pitfalls of different HPV tests possibly leading to classification errors.
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Affiliation(s)
- Francesca Arezzo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (V.C.); (C.L.); (E.C.)
- Correspondence: (F.A.); (G.I.); Tel.: +39-3274961788 (F.A.); +39-3388536505 (G.I.)
| | - Gennaro Cormio
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (V.C.); (C.L.); (E.C.)
| | - Vera Loizzi
- Obstetrics and Gynecology Unit, Interdisciplinar Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy;
| | - Gerardo Cazzato
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (L.R.)
| | - Viviana Cataldo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (V.C.); (C.L.); (E.C.)
| | - Claudio Lombardi
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (V.C.); (C.L.); (E.C.)
| | - Giuseppe Ingravallo
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (L.R.)
- Correspondence: (F.A.); (G.I.); Tel.: +39-3274961788 (F.A.); +39-3388536505 (G.I.)
| | - Leonardo Resta
- Department of Emergency and Organ Transplantation, Pathology Section, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (L.R.)
| | - Ettore Cicinelli
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy; (G.C.); (V.C.); (C.L.); (E.C.)
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