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Preetam S, Mondal S, Priya S, Bora J, Ramniwas S, Rustagi S, Qusty NF, Alghamdi S, Babalghith AO, Siddiqi A, Malik S. Targeting tumour markers in ovarian cancer treatment. Clin Chim Acta 2024; 559:119687. [PMID: 38663473 DOI: 10.1016/j.cca.2024.119687] [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: 03/10/2024] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
Ovarian cancers (OC) are the most common, lethal, and stage-dependent cancers at the global level, specifically in female patients. Targeted therapies involve the administration of drugs that specifically target the alterations in tumour cells responsible for their growth, proliferation, and metastasis, with the aim of treating particular patients. Presently, within the realm of gynaecological malignancies, specifically in breast and OCs, there exist various prospective therapeutic targets encompassing tumour-intrinsic signalling pathways, angiogenesis, homologous-recombination deficit, hormone receptors, and immunologic components. Breast cancers are often detected in advanced stages, primarily due to the lack of a reliable screening method. However, various tumour markers have been extensively researched and employed to evaluate the condition, progression, and effectiveness of medication treatments for this ailment. The emergence of recent technological advancements in the domains of bioinformatics, genomics, proteomics, and metabolomics has facilitated the exploration and identification of hitherto unknown biomarkers. The primary objective of this comprehensive review is to meticulously investigate and analyze both established and emerging methodologies employed in the identification of tumour markers associated with OC.
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Affiliation(s)
- Subham Preetam
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST) Dalseong-gun, Daegu 42988, South Korea.
| | - Sagar Mondal
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Swati Priya
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Jutishna Bora
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Seema Ramniwas
- University Center for Research and Development, Department of Biotechnology, Chandigarh University, Gharuan, Mohali 140413, India.
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, 248007 Dehradun, Uttarakhand, India.
| | - Naeem F Qusty
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Saad Alghamdi
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Ahmad O Babalghith
- Medical Genetics Department, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Abdullah Siddiqi
- Department of Clinical Laboratory, Makkah Park Clinics, Makkah, Saudi Arabia.
| | - Sumira Malik
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
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Nopour R. Screening ovarian cancer by using risk factors: machine learning assists. Biomed Eng Online 2024; 23:18. [PMID: 38347611 PMCID: PMC10863117 DOI: 10.1186/s12938-024-01219-x] [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: 09/12/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND AND AIM Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes. MATERIALS AND METHODS As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC). RESULTS Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91-0.95]) was recognized as the best-performing model for predicting OC. CONCLUSIONS ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.
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Affiliation(s)
- Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
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Abuzinadah N, Kumar Posa S, Alarfaj AA, Alabdulqader EA, Umer M, Kim TH, Alsubai S, Ashraf I. Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI. Cancers (Basel) 2023; 15:5793. [PMID: 38136346 PMCID: PMC10742117 DOI: 10.3390/cancers15245793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.
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Affiliation(s)
- Nihal Abuzinadah
- Faculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia;
| | - Sarath Kumar Posa
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA;
| | - Aisha Ahmed Alarfaj
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ebtisam Abdullah Alabdulqader
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Tai-Hoon Kim
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50, Daehak-ro, Yeosu-si 59626, Jeollanam-do, Republic of Korea
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia;
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Lai Y, Xu D, Li K, Song L, Chen Y, Li H, Hu Z, Zhou F, Zhou J, Shen Y. Multi-view progression diagnosis of thyroid cancer by integrating platelet transcriptomes and blood routine tests. Comput Biol Med 2023; 167:107613. [PMID: 37918259 DOI: 10.1016/j.compbiomed.2023.107613] [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: 09/06/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
Thyroid cancer is the most common type of endocrine system cancer. The pre-cancer and early stages are usually benign or slowly growing, and do not need invasive treatments. This study investigated the challenging classification task of four classes of samples, i.e., normal controls (N), thyroid adenomas (TA), papillary thyroid cancers (PTC) and metastasized papillary thyroid cancers (MPTC). We proposed a multi-view progression diagnosis framework ThyroidBloodTest to integrate the two views of RNAseq platelet transcriptomes (View-T) and blood routine (View-B) features. Platelet transcriptome represented the molecular-level information, while the blood routine features were easy to obtain in the clinical practice. Eleven feature selection algorithms and seven classifiers were evaluated for both views. The experimental data suggested the importance of choosing appropriate data analysis algorithms and feature engineering techniques like principal component analysis (PCA). The best ThyroidBloodTest model achieved Acc = 0.8750 for the four-class classification of the N/TA/PTC/MPTC samples based on the integrated feature space of View-T and View-B. The cellular localization cytosol and three post-translational modification types acetylation/phosphorylation/ubiquitination were observed to be enriched in the proteins encoded by the View-T biomarkers. The numbers of different immune cells also contributed positively to the progression diagnosis of thyroid cancer. The proposed multi-view prediction model demonstrated the necessity of integrating both platelet transcriptomes and blood routine tests for the progression diagnosis of thyroid cancer.
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Affiliation(s)
- Yi Lai
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China; Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Dong Xu
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Kewei Li
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Lin Song
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yiming Chen
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - He Li
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Zhaoyang Hu
- Shanghai Institute of Fun-Med Digital Health Technology, 115 Xinjunhuan Road, Minhang District, Shanghai, 201100, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
| | - Jiaqing Zhou
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Yuling Shen
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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Ledger A, Ceusters J, Valentin L, Testa A, Van Holsbeke C, Franchi D, Bourne T, Froyman W, Timmerman D, Van Calster B. Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm. BMC Med Res Methodol 2023; 23:276. [PMID: 38001421 PMCID: PMC10668424 DOI: 10.1186/s12874-023-02103-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage I primary invasive, stage II-IV primary invasive, or secondary metastatic. METHODS This retrospective cohort study used 5909 patients recruited from 1999 to 2012 for model development, and 3199 patients recruited from 2012 to 2015 for model validation. Patients were recruited at oncology referral or general centers and underwent an ultrasound examination and surgery ≤ 120 days later. We developed models using standard multinomial logistic regression (MLR), Ridge MLR, random forest (RF), XGBoost, neural networks (NN), and support vector machines (SVM). We used nine clinical and ultrasound predictors but developed models with or without CA125. RESULTS Most tumors were benign (3980 in development and 1688 in validation data), secondary metastatic tumors were least common (246 and 172). The c-statistic (AUROC) to discriminate benign from any type of malignant tumor ranged from 0.89 to 0.92 for models with CA125, from 0.89 to 0.91 for models without. The multiclass c-statistic ranged from 0.41 (SVM) to 0.55 (XGBoost) for models with CA125, and from 0.42 (SVM) to 0.51 (standard MLR) for models without. Multiclass calibration was best for RF and XGBoost. Estimated probabilities for a benign tumor in the same patient often differed by more than 0.2 (20% points) depending on the model. Net Benefit for diagnosing malignancy was similar for algorithms at the commonly used 10% risk threshold, but was slightly higher for RF at higher thresholds. Comparing models, between 3% (XGBoost vs. NN, with CA125) and 30% (NN vs. SVM, without CA125) of patients fell on opposite sides of the 10% threshold. CONCLUSION Although several models had similarly good performance, individual probability estimates varied substantially.
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Affiliation(s)
- Ashleigh Ledger
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium
| | - Jolien Ceusters
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium
- Department of Oncology, Leuven Cancer Institute, Laboratory of Tumor Immunology and Immunotherapy, KU Leuven, Leuven, Belgium
| | - Lil Valentin
- Department of Obstetrics and Gynecology, Skåne University Hospital, Malmö, Sweden
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Antonia Testa
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Dorella Franchi
- Preventive Gynecology Unit, Division of Gynecology, European Institute of Oncology IRCCS, Milan, Italy
| | - Tom Bourne
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium
- Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium
- Queen Charlotte's and Chelsea Hospital, Imperial College, London, UK
| | - Wouter Froyman
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium
- Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium
| | - Dirk Timmerman
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium
- Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000, Belgium.
- Department of Biomedical Data Sciences, Leiden University Medical Centre (LUMC), Leiden, Netherlands.
- Leuven Unit for Health Technology Assessment Research (LUHTAR), KU Leuven, Leuven, Belgium.
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Hatamikia S, Nougaret S, Panico C, Avesani G, Nero C, Boldrini L, Sala E, Woitek R. Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers. Eur Radiol Exp 2023; 7:50. [PMID: 37700218 PMCID: PMC10497482 DOI: 10.1186/s41747-023-00364-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/19/2023] [Indexed: 09/14/2023] Open
Abstract
High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used.
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Affiliation(s)
- Sepideh Hatamikia
- Research Center for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria.
- Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria.
| | - Stephanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, University of Montpellier, Montpellier, France
| | - Camilla Panico
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giacomo Avesani
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Camilla Nero
- Scienze Della Salute Della Donna, del bambino e Di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Ramona Woitek
- Research Center for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
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Comes MC, Arezzo F, Cormio G, Bove S, Calabrese A, Fanizzi A, Kardhashi A, La Forgia D, Legge F, Romagno I, Loizzi V, Massafra R. An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy. Front Oncol 2023; 13:1181792. [PMID: 37519818 PMCID: PMC10374844 DOI: 10.3389/fonc.2023.1181792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO. Methods In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO. Results The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%. Discussion In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.
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Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesca Arezzo
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica - (DiMePRe-J), Università di Bari “Aldo Moro”, Bari, Italy
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Gennaro Cormio
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Angela Calabrese
- Unità Operativa Semplice di Radiodiagnostica Avanzata, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Anila Kardhashi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesco Legge
- Unità di Ginecologia Oncologica, “F. Miulli” Ospedale Generale Regionale, Acquaviva delle Fonti, Bari, Italy
| | | | - Vera Loizzi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
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Zhao Z, Sun C, Hou J, Yu P, Wei Y, Bai R, Yang P. Identification of STEAP3-based molecular subtype and risk model in ovarian cancer. J Ovarian Res 2023; 16:126. [PMID: 37386521 DOI: 10.1186/s13048-023-01218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis. METHODS STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC). RESULTS STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients. CONCLUSION In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.
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Affiliation(s)
- Zouyu Zhao
- First Affiliated Hospital, Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Chongfeng Sun
- First Affiliated Hospital, Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jishuai Hou
- First Affiliated Hospital, Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Panpan Yu
- First Affiliated Hospital, Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Yan Wei
- First Affiliated Hospital, Shihezi University, Shihezi, China
| | - Rui Bai
- First Affiliated Hospital, Shihezi University, Shihezi, China
| | - Ping Yang
- First Affiliated Hospital, Shihezi University, Shihezi, China.
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China.
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9
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Han N, He J, Shi L, Zhang M, Zheng J, Fan Y. Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study. Front Genet 2022; 13:1020899. [PMID: 36419827 PMCID: PMC9676265 DOI: 10.3389/fgene.2022.1020899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/24/2022] [Indexed: 10/13/2023] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.
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Affiliation(s)
- Na Han
- Department of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Juan He
- Department of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Lixin Shi
- Department of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Miao Zhang
- Department of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Jing Zheng
- Department of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Yuanshuo Fan
- Department of Endocrinology, Guizhou Provincial People's Hospital, Guiyang, China
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10
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Prediction of endometrial cancer recurrence by using a novel machine learning algorithm: An Israeli gynecologic oncology group study. J Gynecol Obstet Hum Reprod 2022; 51:102466. [DOI: 10.1016/j.jogoh.2022.102466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/04/2022] [Accepted: 08/25/2022] [Indexed: 11/23/2022]
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11
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A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8501819. [PMID: 36277898 PMCID: PMC9581702 DOI: 10.1155/2022/8501819] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 09/22/2022] [Accepted: 09/27/2022] [Indexed: 11/28/2022]
Abstract
Background Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively. Conclusion This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.
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12
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Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha BA, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022; 23:387. [PMID: 36153474 PMCID: PMC9509545 DOI: 10.1186/s12859-022-04926-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.
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Huang X, Chen X, Chen X, Wang W. Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database. Public Health Genomics 2022; 25:1-9. [PMID: 35917800 DOI: 10.1159/000525316] [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: 02/07/2022] [Accepted: 05/12/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient's survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer. METHODS A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA. RESULTS We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively. CONCLUSION By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.
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Affiliation(s)
- Xiaoyan Huang
- Medical Oncology, 900 Hospital of the Joint Logistics Team, Fuzhou, China
| | - Xiong Chen
- Medical Oncology, 900 Hospital of the Joint Logistics Team, Fuzhou, China
| | - Xi Chen
- Medical Oncology, 900 Hospital of the Joint Logistics Team, Fuzhou, China
| | - Wenling Wang
- Medical Oncology, 900 Hospital of the Joint Logistics Team, Fuzhou, China
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14
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Ovarian cancer detection using optimized machine learning models with adaptive differential evolution. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Ahamad MM, Aktar S, Uddin MJ, Rahman T, Alyami SA, Al-Ashhab S, Akhdar HF, Azad AKM, Moni MA. Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. J Pers Med 2022; 12:jpm12081211. [PMID: 35893305 PMCID: PMC9394434 DOI: 10.3390/jpm12081211] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/07/2022] [Accepted: 07/21/2022] [Indexed: 12/03/2022] Open
Abstract
One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.
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Affiliation(s)
- Md. Martuza Ahamad
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.M.A.); (S.A.); (M.J.U.)
| | - Sakifa Aktar
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.M.A.); (S.A.); (M.J.U.)
| | - Md. Jamal Uddin
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.M.A.); (S.A.); (M.J.U.)
| | - Tasnia Rahman
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6200, Bangladesh;
| | - Salem A. Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia; (S.A.A.); (S.A.-A.)
| | - Samer Al-Ashhab
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia; (S.A.A.); (S.A.-A.)
| | - Hanan Fawaz Akhdar
- Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia;
| | - AKM Azad
- Faculty of Science, Engineering & Technology, Swinburne University of Technology, Sydney 2150, Australia; or
- ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
- Correspondence:
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16
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Laios A, Kalampokis E, Johnson R, Munot S, Thangavelu A, Hutson R, Broadhead T, Theophilou G, Leach C, Nugent D, De Jong D. Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14143447. [PMID: 35884506 PMCID: PMC9316555 DOI: 10.3390/cancers14143447] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598−0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69−0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655−0.823; p < 0.95). We identified “turning points” that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
- Correspondence:
| | | | - Racheal Johnson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Sarika Munot
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Richard Hutson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Tim Broadhead
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Georgios Theophilou
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Chris Leach
- School of Human & Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK;
- Department of Psychology Services, South West Yorkshire Mental Health NHS Foundation Trust, The Laura Mitchell Health & Wellbeing Centre, Halifax HX1 1YR, UK
| | - David Nugent
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Diederick De Jong
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
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Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model. Diagnostics (Basel) 2022; 12:diagnostics12040958. [PMID: 35454006 PMCID: PMC9029337 DOI: 10.3390/diagnostics12040958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/24/2022] [Accepted: 03/04/2022] [Indexed: 02/04/2023] Open
Abstract
Through a multitude of studies, the gut microbiota has been recognized as a significant influencer of both homeostasis and pathophysiology. Certain microbial taxa can even affect treatments such as cancer immunotherapies, including the immune checkpoint blockade. These taxa can impact such processes both individually as well as collectively through mechanisms from quorum sensing to metabolite production. Due to this overarching presence of the gut microbiota in many physiological processes distal to the GI tract, we hypothesized that mice bearing tumors at extraintestinal sites would display a distinct intestinal microbial signature from non-tumor-bearing mice, and that such a signature would involve taxa that collectively shift with tumor presence. Microbial OTUs were determined from 16S rRNA genes isolated from the fecal samples of C57BL/6 mice challenged with either B16-F10 melanoma cells or PBS control and analyzed using QIIME. Relative proportions of bacteria were determined for each mouse and, using machine-learning approaches, significantly altered taxa and co-occurrence patterns between tumor- and non-tumor-bearing mice were found. Mice with a tumor had elevated proportions of Ruminococcaceae, Peptococcaceae.g_rc4.4, and Christensenellaceae, as well as significant information gains and ReliefF weights for Bacteroidales.f__S24.7, Ruminococcaceae, Clostridiales, and Erysipelotrichaceae. Bacteroidales.f__S24.7, Ruminococcaceae, and Clostridiales were also implicated through shifting co-occurrences and PCA values. Using these seven taxa as a melanoma signature, a neural network reached an 80% tumor detection accuracy in a 10-fold stratified random sampling validation. These results indicated gut microbial proportions as a biosensor for tumor detection, and that shifting co-occurrences could be used to reveal relevant taxa.
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Laios A, Kalampokis E, Johnson R, Thangavelu A, Tarabanis C, Nugent D, De Jong D. Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer. J Pers Med 2022; 12:jpm12040607. [PMID: 35455723 PMCID: PMC9030484 DOI: 10.3390/jpm12040607] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8−0.93). We identified “turning points” that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index < 4 and <5, respectively, followed by Surgical Complexity Score > 4, patient’s age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (A.T.); (D.N.); (D.D.J.)
- Correspondence:
| | - Evangelos Kalampokis
- Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece;
- Center for Research & Technology HELLAS (CERTH), 6th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece
| | - Racheal Johnson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (A.T.); (D.N.); (D.D.J.)
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (A.T.); (D.N.); (D.D.J.)
| | - Constantine Tarabanis
- Department of Internal Medicine, School of Medicine, New York University, NYU, Langone Health, New York, NY 10016, USA;
| | - David Nugent
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (A.T.); (D.N.); (D.D.J.)
| | - Diederick De Jong
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (A.T.); (D.N.); (D.D.J.)
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Ben M'Barek I, Jauvion G, Ceccaldi PF. [Artificial Intelligence in medicine: What about gynecology-obstetric?]. GYNECOLOGIE, OBSTETRIQUE, FERTILITE & SENOLOGIE 2022; 50:340-343. [PMID: 35183787 DOI: 10.1016/j.gofs.2022.02.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/17/2022] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Affiliation(s)
- I Ben M'Barek
- Service de gynécologie obstétrique, Assistance publique-Hôpitaux de Paris-Beaujon, 100, boulevard du Général-Leclerc, Clichy, France; Université de Paris, 75006 Paris, France; Département de simulation en Santé, Université de Paris, Paris, France.
| | | | - P-F Ceccaldi
- Service de gynécologie obstétrique, Assistance publique-Hôpitaux de Paris-Beaujon, 100, boulevard du Général-Leclerc, Clichy, France; Université de Paris, 75006 Paris, France; Département de simulation en Santé, Université de Paris, Paris, France
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Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, only four 3D joints were used in our system. After the recognition of the posture, a warning message will be sent to the caregiver for assistance if the patient stays in the same posture for more than a predetermined period (e.g., two hours). Experimental results indicate that our proposed method is capable of achieving a high accuracy in posture recognition (above 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive test in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Blue-Depth) camera system. In addition, a desktop computer can be used for multi-point monitoring to reduce cost, since real-time processing is not required in this application.
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Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. THE LANCET DIGITAL HEALTH 2022; 4:e179-e187. [DOI: 10.1016/s2589-7500(21)00278-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/15/2021] [Accepted: 12/02/2021] [Indexed: 11/19/2022]
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Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI. Eur Radiol 2022; 32:3661-3669. [PMID: 35037969 DOI: 10.1007/s00330-021-08493-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/11/2021] [Accepted: 11/28/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. METHODS DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. RESULTS svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. CONCLUSIONS The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset. KEY POINTS • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.
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Sato M, Sato S, Shintani D, Hanaoka M, Ogasawara A, Miwa M, Yabuno A, Kurosaki A, Yoshida H, Fujiwara K, Hasegawa K. Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma. BMC Cancer 2022; 22:59. [PMID: 35027024 PMCID: PMC8756654 DOI: 10.1186/s12885-021-09148-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Administration of poly (ADP-ribose) polymerase (PARP) inhibitors after achieving a response to platinum-containing drugs significantly prolonged relapse-free survival compared to placebo administration. PARP inhibitors have been used in clinical practice. However, patients with platinum-resistant relapsed ovarian cancer still have a poor prognosis and there is an unmet need. The purpose of this study was to examine the clinical significance of metabolic genes and focal adhesion kinase (FAK) activity in advanced ovarian high-grade serous carcinoma (HGSC). METHODS The RNA sequencing (RNA-seq) data and clinical data of HGSC patients were obtained from the Genomic Data Commons (GDC) Data Portal and analysed ( https://portal.gdc.cancer.gov/ ). In addition, tumour tissue was sampled by laparotomy or screening laparoscopy prior to treatment initiation from patients diagnosed with stage IIIC ovarian cancer (International Federation of Gynecology and Obstetrics (FIGO) classification, 2014) at the Saitama Medical University International Medical Center, and among the patients diagnosed with HGSC, 16 cases of available cryopreserved specimens were included in this study. The present study was reviewed and approved by the Institutional Review Board of Saitama Medical University International Medical Center (Saitama, Japan). Among the 6307 variable genes detected in both The Cancer Genome Atlas-Ovarian (TCGA-OV) data and clinical specimen data, 35 genes related to metabolism and FAK activity were applied. RNA-seq data were analysed using the Subio Platform (Subio Inc, Japan). JMP 15 (SAS, USA) was used for statistical analysis and various types of machine learning. The Kaplan-Meier method was used for survival analysis, and the Wilcoxon test was used to analyse significant differences. P < 0.05 was considered significant. RESULTS In the TCGA-OV data, patients with stage IIIC with a residual tumour diameter of 1-10 mm were selected for K means clustering and classified into groups with significant prognostic correlations (p = 0.0444). These groups were significantly associated with platinum sensitivity/resistance in clinical cases (χ2 test, p = 0.0408) and showed significant relationships with progression-free survival (p = 0.0307). CONCLUSION In the TCGA-OV data, 2 groups classified by clustering focusing on metabolism-related genes and FAK activity were shown to be associated with platinum resistance and a poor prognosis.
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Affiliation(s)
- Masakazu Sato
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan.
| | - Sho Sato
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Daisuke Shintani
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Mieko Hanaoka
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Aiko Ogasawara
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Maiko Miwa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Akira Yabuno
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Akira Kurosaki
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | - Hiroyuki Yoshida
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
| | | | - Kosei Hasegawa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka, Saitama, 350-1298, Japan
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med 2021; 120:102164. [PMID: 34629152 DOI: 10.1016/j.artmed.2021.102164] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/28/2021] [Accepted: 08/31/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers. METHODS A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy. RESULTS Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review. CONCLUSIONS In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.
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Affiliation(s)
- Munetoshi Akazawa
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
| | - Kazunori Hashimoto
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
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Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
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Ma J, Yang J, Jin Y, Cheng S, Huang S, Zhang N, Wang Y. Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study. Onco Targets Ther 2021; 14:3267-3280. [PMID: 34040391 PMCID: PMC8140950 DOI: 10.2147/ott.s307546] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/03/2021] [Indexed: 11/23/2022] Open
Abstract
Objective We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs). Patients and Methods We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test. Results Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727–0.866) and 0.809 (95% CI, 0.742–0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602–4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752–6.390) for overall survival (OS). Conclusion Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients. Trial registration ChiCTR-DDD-16009601 Registered 25 October 2016.
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Affiliation(s)
- Jun Ma
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yue Jin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shan Huang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Nan Zhang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Feng Y, Wang X, Zhang J. A heterogeneous ensemble learning method for neuroblastoma survival prediction. IEEE J Biomed Health Inform 2021; 26:1472-1483. [PMID: 33848254 DOI: 10.1109/jbhi.2021.3073056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble learning method to predict the survival of neuroblastoma patients and extract decision rules from the proposed method to assist doctors in making decisions. After data preprocessing, five heterogeneous base learners were developed, which consisted of decision tree, random forest, support vector machine based on genetic algorithm, extreme gradient boosting and light gradient boosting machine. Subsequently, a heterogeneous feature selection method was devised to obtain the optimal feature subset of each base learner, and the optimal feature subset of each base learner guided the construction of the base learners as a priori knowledge. Furthermore, an area under curve-based ensemble mechanism was proposed to integrate the five heterogeneous base learners. Finally, the proposed method was compared with mainstream machine learning methods from different indicators, and valuable information was extracted by using the partial dependency plot analysis method and rule-extracted method from the proposed method. Experimental results show that the proposed method achieves an accuracy of 91.64%, recall of 91.14%, and AUC of 91.35% and is significantly better than the mainstream machine learning methods. In addition, interpretable rules with accuracy higher than 0.900 and predicted responses are extracted from the proposed method. Our study can effectively improve the performance of the clinical decision support system to improve the survival of neuroblastoma patients.
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Gated Graph Attention Network for Cancer Prediction. SENSORS 2021; 21:s21061938. [PMID: 33801894 PMCID: PMC7998488 DOI: 10.3390/s21061938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 01/17/2023]
Abstract
With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work's limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods.
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