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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects. Int J Med Inform 2024; 188:105464. [PMID: 38728812 DOI: 10.1016/j.ijmedinf.2024.105464] [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: 10/15/2023] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Moharrami M, Azimian Zavareh P, Watson E, Singhal S, Johnson AEW, Hosni A, Quinonez C, Glogauer M. Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review. PLoS One 2024; 19:e0307531. [PMID: 39046953 PMCID: PMC11268644 DOI: 10.1371/journal.pone.0307531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. METHODS A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. CONCLUSIONS ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Parnia Azimian Zavareh
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Chronic Disease and Injury Prevention Department, Health Promotion, Public Health Ontario, Toronto, Canada
| | - Alistair E. W. Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Ali Hosni
- Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Canada
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Canada
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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024:S0030-6665(24)00070-7. [PMID: 38910064 DOI: 10.1016/j.otc.2024.05.001] [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] [Indexed: 06/25/2024]
Abstract
This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.
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Affiliation(s)
- Jamie Oliver
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Rahul Alapati
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Jason Lee
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Andrés Bur
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
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Alabi RO, Almangush A, Elmusrati M, Leivo I, Mäkitie AA. Interpretable machine learning model for prediction of overall survival in laryngeal cancer. Acta Otolaryngol 2024:1-7. [PMID: 38279817 DOI: 10.1080/00016489.2023.2301648] [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: 10/16/2023] [Accepted: 12/21/2023] [Indexed: 01/29/2024]
Abstract
Background: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, University of Turku, Pathology, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, University of Turku, Pathology, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Rokhshad R, Mohammad-Rahimi H, Price JB, Shoorgashti R, Abbasiparashkouh Z, Esmaeili M, Sarfaraz B, Rokhshad A, Motamedian SR, Soltani P, Schwendicke F. Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis. Clin Oral Investig 2024; 28:88. [PMID: 38217733 DOI: 10.1007/s00784-023-05475-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Jeffery B Price
- Department of Oncology and Diagnostic Sciences, University of Maryland, School of Dentistry, Baltimore, Maryland 650 W Baltimore St, Baltimore, MD, 21201, USA
| | - Reyhaneh Shoorgashti
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | | | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Bita Sarfaraz
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Arad Rokhshad
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Salamat Blv, Isfahan Dental School, Isfahan, Iran
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Nepales, Italy
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Charitépl. 1, 10117, Berlin, Germany
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Cai Y, Xie Y, Zhang S, Wang Y, Wang Y, Chen J, Huang Z. Prediction of postoperative recurrence of oral cancer by artificial intelligence model: Multilayer perceptron. Head Neck 2023; 45:3053-3066. [PMID: 37789719 DOI: 10.1002/hed.27533] [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: 04/06/2023] [Revised: 09/06/2023] [Accepted: 09/17/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Postoperative recurrence of oral cancer is an important factor affecting the prognosis of patients. Artificial intelligence is used to establish a machine learning model to predict the risk of postoperative recurrence of oral cancer. METHODS The information of 387 patients with postoperative oral cancer were collected to establish the multilayer perceptron (MLP) model. The comprehensive variable model was compared with the characteristic variable model, and the MLP model was compared with other models to evaluate the sensitivity of different models in the prediction of postoperative recurrence of oral cancer. RESULTS The overall performance of the MLP model under comprehensive variable input was the best. CONCLUSION The MLP model has good sensitivity to predict postoperative recurrence of oral cancer, and the predictive model with variable input training is better than that with characteristic variable input.
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Affiliation(s)
- Yongkang Cai
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Yutong Xie
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Shulian Zhang
- School of Software Engineering, South China University of Technology, Guangzhou, China
| | - Yuepeng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Yan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Jian Chen
- School of Software Engineering, South China University of Technology, Guangzhou, China
| | - Zhiquan Huang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
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Chiesa‐Estomba CM, González‐García JA, Larruscain E, Sistiaga Suarez JA, Quer M, León X, Martínez‐Ruiz de Apodaca P, López‐Mollá C, Mayo‐Yanez M, Medela A. Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach. World J Otorhinolaryngol Head Neck Surg 2023; 9:271-279. [PMID: 38059137 PMCID: PMC10696266 DOI: 10.1002/wjo2.94] [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: 07/15/2022] [Revised: 11/27/2022] [Accepted: 12/16/2022] [Indexed: 04/03/2023] Open
Abstract
Introduction Machine learning (ML)-based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now. Three distinct ML models, random forest (RF), K-nearest neighbor, and artificial neural network (ANN), for the prediction of FNI were evaluated in this mode. Methods A retrospective, longitudinal, multicentric study was performed, including patients who went through parotid gland surgery for benign tumors at three different university hospitals. Results Seven hundred and thirty-six patients were included. The most compelling aspects related to risk escalation of FNI were as follows: (1) location, in the mid-portion of the gland, near to or above the main trunk of the facial nerve and at the top part, over the frontal or the orbital branch of the facial nerve; (2) tumor volume in the anteroposterior axis; (3) the necessity to simultaneously dissect more than one level; and (4) the requirement of an extended resection compared to a lesser extended resection. By contrast, in accordance with the ML analysis, the size of the tumor (>3 cm), as well as gender and age did not result in a determining favor in relation to the risk of FNI. Discussion The findings of this research conclude that ML models such as RF and ANN may serve evidence-based predictions from multicentric data regarding the risk of FNI. Conclusion Along with the advent of ML technology, an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical, radiological, histological, and/or cytological data.
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Affiliation(s)
- Carlos M. Chiesa‐Estomba
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Biodonostia Health Research InstituteSan SebastiánSpain
| | - Jose A. González‐García
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Ekhiñe Larruscain
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Jon A. Sistiaga Suarez
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Miquel Quer
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Xavier León
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Paula Martínez‐Ruiz de Apodaca
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Celia López‐Mollá
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Miguel Mayo‐Yanez
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Otorhinolaryngology—Head and Neck Surgery DepartmentComplexo Hospitalario Universitario A Coruña (CHUAC)A CoruñaGaliciaSpain
- Clinical Research in Medicine, International Center for Doctorate and Advanced Studies (CIEDUS), Universidade de Santiago de, Compostela (USC)Santiago de CompostelaGaliciaSpain
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Fonseca AU, Felix JP, Pinheiro H, Vieira GS, Mourão ÝC, Monteiro JCG, Soares F. An Intelligent System to Improve Diagnostic Support for Oral Squamous Cell Carcinoma. Healthcare (Basel) 2023; 11:2675. [PMID: 37830712 PMCID: PMC10572543 DOI: 10.3390/healthcare11192675] [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: 08/18/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/14/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most-prevalent cancer types worldwide, and it poses a serious threat to public health due to its high mortality and morbidity rates. OSCC typically has a poor prognosis, significantly reducing the chances of patient survival. Therefore, early detection is crucial to achieving a favorable prognosis by providing prompt treatment and increasing the chances of remission. Salivary biomarkers have been established in numerous studies to be a trustworthy and non-invasive alternative for early cancer detection. In this sense, we propose an intelligent system that utilizes feed-forward artificial neural networks to classify carcinoma with salivary biomarkers extracted from control and OSCC patient samples. We conducted experiments using various salivary biomarkers, ranging from 1 to 51, to train the model, and we achieved excellent results with precision, sensitivity, and specificity values of 98.53%, 96.30%, and 97.56%, respectively. Our system effectively classified the initial cases of OSCC with different amounts of biomarkers, aiding medical professionals in decision-making and providing a more-accurate diagnosis. This could contribute to a higher chance of treatment success and patient survival. Furthermore, the minimalist configuration of our model presents the potential for incorporation into resource-limited devices or environments.
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Affiliation(s)
- Afonso U. Fonseca
- Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, GO, Brazil; (J.P.F.); (H.P.); (G.S.V.); (F.S.)
| | - Juliana P. Felix
- Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, GO, Brazil; (J.P.F.); (H.P.); (G.S.V.); (F.S.)
| | - Hedenir Pinheiro
- Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, GO, Brazil; (J.P.F.); (H.P.); (G.S.V.); (F.S.)
| | - Gabriel S. Vieira
- Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, GO, Brazil; (J.P.F.); (H.P.); (G.S.V.); (F.S.)
- Federal Institute Goiano, Computer Vision Lab, Urutaí 75790-000, GO, Brazil
| | | | | | - Fabrizzio Soares
- Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, GO, Brazil; (J.P.F.); (H.P.); (G.S.V.); (F.S.)
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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Mäkitie AA, Alabi RO, Ng SP, Takes RP, Robbins KT, Ronen O, Shaha AR, Bradley PJ, Saba NF, Nuyts S, Triantafyllou A, Piazza C, Rinaldo A, Ferlito A. Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews. Adv Ther 2023; 40:3360-3380. [PMID: 37291378 PMCID: PMC10329964 DOI: 10.1007/s12325-023-02527-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/20/2023] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management. METHODS Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines. RESULTS Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks. CONCLUSION At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.
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Affiliation(s)
- Antti A Mäkitie
- Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki, P.O. Box 263, 00029, HUS, Helsinki, Finland.
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden.
| | - Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Sweet Ping Ng
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, Australia
- School of Imaging and Radiation Sciences, Monash University, Melbourne, Australia
| | - Robert P Takes
- Department of Otolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - K Thomas Robbins
- Department of Otolaryngology Head Neck Surgery, SIU School of Medicine, Southern Illinois University, Springfield, IL, USA
| | - Ohad Ronen
- Department of Otolaryngology-Head and Neck Surgery, Galilee Medical Center Affiliated with Azrieil Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Ashok R Shaha
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Patrick J Bradley
- The University of Nottingham, Department of ORLHNS, Queens Medical Centre Campus, Nottingham University Hospital, Derby Road, Nottingham, NG7 2UH, UK
| | - Nabil F Saba
- Department of Hematology and Medical Oncology, The Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Sandra Nuyts
- Laboratory of Experimental Radiotherapy, Department of Oncology, KU Leuven, 3000, Leuven, Belgium
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, 3000, Leuven, Belgium
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, School of Dentistry, University of Liverpool, Liverpool, UK
| | - Cesare Piazza
- Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
| | | | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, Padua, Italy
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11
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Guan X, Du Y, Ma R, Teng N, Ou S, Zhao H, Li X. Construction of the XGBoost model for early lung cancer prediction based on metabolic indices. BMC Med Inform Decis Mak 2023; 23:107. [PMID: 37312179 DOI: 10.1186/s12911-023-02171-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 04/05/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches. RESULTS In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer. CONCLUSIONS This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.
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Affiliation(s)
- Xiuliang Guan
- School of Public Health, Dalian Medical University, Dalian, 116000, China
| | - Yue Du
- School of Public Health, Dalian Medical University, Dalian, 116000, China
| | - Rufei Ma
- School of Public Health, Dalian Medical University, Dalian, 116000, China
| | - Nan Teng
- School of Public Health, Dalian Medical University, Dalian, 116000, China
| | - Shu Ou
- School of Public Health, Dalian Medical University, Dalian, 116000, China
| | - Hui Zhao
- Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Xiaofeng Li
- School of Public Health, Dalian Medical University, Dalian, 116000, China.
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12
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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep 2023; 13:8984. [PMID: 37268685 DOI: 10.1038/s41598-023-35795-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm-extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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13
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Gomes RFT, Schuch LF, Martins MD, Honório EF, de Figueiredo RM, Schmith J, Machado GN, Carrard VC. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review. J Digit Imaging 2023; 36:1060-1070. [PMID: 36650299 PMCID: PMC10287602 DOI: 10.1007/s10278-023-00775-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil.
| | - Lauren Frenzel Schuch
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Manoela Domingues Martins
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Rodrigo Marques de Figueiredo
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Jean Schmith
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Giovanna Nunes Machado
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Vinicius Coelho Carrard
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
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14
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Zha Y, Xue C, Liu Y, Ni J, De La Fuente JM, Cui D. Artificial intelligence in theranostics of gastric cancer, a review. MEDICAL REVIEW (2021) 2023; 3:214-229. [PMID: 37789960 PMCID: PMC10542883 DOI: 10.1515/mr-2022-0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/26/2023] [Indexed: 10/05/2023]
Abstract
Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.
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Affiliation(s)
- Yiqian Zha
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Cuili Xue
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Yanlei Liu
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Jian Ni
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | | | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
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15
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Ling D, Liu A, Sun J, Wang Y, Wang L, Song X, Zhao X. Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC. Interdiscip Sci 2023:10.1007/s12539-023-00569-9. [PMID: 37248421 DOI: 10.1007/s12539-023-00569-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.
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Affiliation(s)
- Dan Ling
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Anhao Liu
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Junwei Sun
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yanfeng Wang
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China.
| | - Lidong Wang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China
| | - Xin Song
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China
| | - Xueke Zhao
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China
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16
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review. J Clin Epidemiol 2023; 157:120-133. [PMID: 36935090 DOI: 10.1016/j.jclinepi.2023.03.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/03/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
OBJECTIVES In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Meta-Research Centre, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK, ST5 5BG
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Omobolaji Alabi R, Sjöblom A, Carpén T, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL. Int J Med Inform 2023; 175:105064. [PMID: 37094545 DOI: 10.1016/j.ijmedinf.2023.105064] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation). OBJECTIVES This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. METHODS We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. CONCLUSION The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Anni Sjöblom
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Timo Carpén
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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18
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Dixit S, Kumar A, Srinivasan K. A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13071353. [PMID: 37046571 PMCID: PMC10093759 DOI: 10.3390/diagnostics13071353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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19
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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning. Acta Otolaryngol 2023; 143:206-214. [PMID: 36794334 DOI: 10.1080/00016489.2023.2172208] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
BACKGROUND A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage. OBJECTIVES We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT). MATERIAL AND METHODS A total of 428 patients from Surveillance, Epidemiology, and End Results (SEER) database were reviewed. Kaplan-Meier and Cox proportional hazards models examine OS. In addition, a ML model was developed for OS likelihood stratification. RESULTS Age, marital status, N stage, Sx, and Sx + CRT were considered significant. Patients with Sx + RT showed better OS than Sx + CRT or Sx alone. A similar result was obtained for T3N0 subgroup. For T3N1 subgroup, Sx + CRT appeared more favorable for 5-year OS. In T3N2 and T3N3 subgroups, the numbers of patients were small to make insightful conclusions. The OS predictive ML model showed an accuracy of 86.3% for OS likelihood prediction. CONCLUSIONS AND SIGNIFICANCE Patients stratified as having high likelihood of OS may be managed with Sx + RT. Further external validation studies are needed to confirm these results.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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20
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Huang YC, Cheng YC, Jhou MJ, Chen M, Lu CJ. Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2359. [PMID: 36767726 PMCID: PMC9915180 DOI: 10.3390/ijerph20032359] [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: 12/27/2022] [Revised: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.
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Affiliation(s)
- Yung-Chuan Huang
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Yu-Chen Cheng
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Mingchih Chen
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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21
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Srivastava R. Role of smartphone devices in precision oncology. J Cancer Res Clin Oncol 2023; 149:393-400. [PMID: 36253632 DOI: 10.1007/s00432-022-04413-3] [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: 09/12/2022] [Accepted: 10/08/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND To improve the care for cancer patients, personalized treatment including monitoring and managing Quality of life (QoL) data collection of patients in his/her home environment, its integration and its analysis is necessary. Advanced technologies have been used to develop smartphone devices to support cancer patients and clinicians by integrating all patient-relevant data, helping with Patient Reported Outcomes (PRO), side effect management, appointments, and nutritional support. PURPOSE In this review the role and challenges of using smartphone applications for precision oncology is discussed. METHODS The methodology section includes the data collection, data integration and predictive modelling approaches. The design, development and evaluation of (AI/ML) algorithms of these apps need intended purpose of these algorithms, description of used mepthods, validity and appropriateness of the datasets, design of the algorithms, evaluation, implementation of these (AI/ML) algorithms and post treatment monitoring. RESULTS Though Artificial intelligence (AI) based results showed higher diagnostic classification accuracy in most of the results, the advancement of these mobile apps technologies has a few limitations. CONCLUSIONS ML techniques and DL are used to discover novel biomarkers for early detection and diagnostics, and AI are used to accelerate drug discovery, exploit biomarkers to accurately match patients to clinical trials, and personalize cancer therapy based only on patient's own data. AI based smartphone apps cannot be treated as autonomous rather used as an integrative tool for patient-relevant data, PRO, side effect management, appointments, nutritional support, emotional and social support, severity of pain detection and correct diagnosis at higher level. It should encourage the clinicians and care givers to support and establish patient-physician relationships with the help of these apps.
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Affiliation(s)
- Ruby Srivastava
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India.
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Lin Y, Tang M, Liu Y, Jiang M, He S, Zeng D, Cui MY. A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma. Transl Cancer Res 2022; 11:4409-4415. [PMID: 36644177 PMCID: PMC9834582 DOI: 10.21037/tcr-22-1669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/07/2022] [Indexed: 12/28/2022]
Abstract
Background Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. Methods The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. Key Content and Findings ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance. Conclusions Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.
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Affiliation(s)
- Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mimi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Liu
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Mengjie Jiang
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Shuangshuang He
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Donglin Zeng
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Min-Yi Cui
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
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23
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An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer. Int J Med Inform 2022; 168:104896. [DOI: 10.1016/j.ijmedinf.2022.104896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/27/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
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24
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Cai L, Li X, Wu L, Wang B, Si M, Tao X. A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma. Curr Oncol 2022; 29:9031-9045. [PMID: 36547122 PMCID: PMC9777250 DOI: 10.3390/curroncol29120708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/07/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.
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Affiliation(s)
- Lingling Cai
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Xiaoguang Li
- Department of Oral Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Disease, Shanghai 201999, China
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Lizhong Wu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Bocheng Wang
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Mingjue Si
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
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25
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Comparison of Bayesian, Frequentist and Machine learning models for predicting the two-year mortality of patients diagnosed with squamous cell carcinoma of the oral cavity. CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH 2022. [DOI: 10.1016/j.cegh.2022.101145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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26
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Bourdillon AT, Shah HP, Cohen O, Hajek MA, Mehra S. Novel Machine Learning Model to Predict Interval of Oral Cancer Recurrence for Surveillance Stratification. Laryngoscope 2022. [DOI: 10.1002/lary.30351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 12/24/2022]
Affiliation(s)
| | - Hemali P. Shah
- Yale University School of Medicine New Haven Connecticut U.S.A
| | - Oded Cohen
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Michael A. Hajek
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Saral Mehra
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
- Yale Cancer Center New Haven Connecticut U.S.A
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27
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Alabi RO, Almangush A, Elmusrati M, Leivo I, Mäkitie A. Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148366. [PMID: 35886221 PMCID: PMC9322510 DOI: 10.3390/ijerph19148366] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/23/2022] [Accepted: 07/04/2022] [Indexed: 12/10/2022]
Abstract
Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00100 Helsinki, Finland; (A.A.); (A.M.)
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland;
- Correspondence:
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00100 Helsinki, Finland; (A.A.); (A.M.)
- Department of Pathology, University of Helsinki, Haartmaninkatu 3 (P.O. Box 21), FIN-00014 Helsinki, Finland
- Institute of Biomedicine, University of Turku, Pathology, 20500 Turku, Finland;
- Faculty of Dentistry, Misurata University, Misurata 2478, Libya
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland;
| | - Ilmo Leivo
- Institute of Biomedicine, University of Turku, Pathology, 20500 Turku, Finland;
| | - Antti Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00100 Helsinki, Finland; (A.A.); (A.M.)
- Department of Otorhinolaryngology—Head and Neck Surgery, University of Helsinki, Helsinki University Hospital, 00029 HUS Helsinki, Finland
- Department of Clinical Sciences, Intervention and Technology, Division of Ear, Nose and Throat Diseases, Karolinska Institute, Karolinska University Hospital, 17177 Stockholm, Sweden
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28
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Can Artificial Intelligence (AI) assist in the diagnosis of oral mucosal lesions and/or oral cancer? Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:413-414. [PMID: 36054978 DOI: 10.1016/j.oooo.2022.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/23/2022]
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29
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Mochão H, Gonçalves D, Alexandre L, Castro C, Valério D, Barahona P, Moreira-Gonçalves D, Costa PMD, Henriques R, Santos LL, Costa RS. IPOscore: An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106754. [PMID: 35364482 DOI: 10.1016/j.cmpb.2022.106754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/07/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. OBJECTIVES The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. RESULTS The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. CONCLUSIONS The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.
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Affiliation(s)
- Hugo Mochão
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Daniel Gonçalves
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal; LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal
| | - Leonardo Alexandre
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal; LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal
| | - Carolina Castro
- Experimental Pathology and Therapeutics Group of Portuguese Institute of Oncology of Porto FG, EPE (IPO-Porto), Porto, Portugal
| | - Duarte Valério
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Pedro Barahona
- NOVA LINCS, Dept. Informatica Faculdade de Ciencias e Tecnologia, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal
| | - Daniel Moreira-Gonçalves
- Research Centre in Physical Activity, Health and Leisure, Faculdade de Desporto, Universidade do Porto, Porto, Portugal
| | - Paulo Matos da Costa
- General Surgery Service, Hospital Garcia de Orta, E.P.E., Portugal; Faculdade de Medicina da Universidade de Lisboa, Portugal
| | - Rui Henriques
- INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal; Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal
| | - Lúcio L Santos
- Surgical ICU of the Portuguese Institute of Oncology, Porto, Portugal; Surgical Oncology Department, IPO-Porto, Porto, Portugal; Experimental Pathology and Therapeutics Group of Portuguese Institute of Oncology of Porto FG, EPE (IPO-Porto), Porto, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol 2022; 22:101. [PMID: 35395724 PMCID: PMC8991704 DOI: 10.1186/s12874-022-01577-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/18/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. FRONTIERS IN ORAL HEALTH 2022; 2:686863. [PMID: 35048032 PMCID: PMC8757862 DOI: 10.3389/froh.2021.686863] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/15/2021] [Indexed: 12/17/2022] Open
Abstract
The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.,Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ibrahim O Bello
- Department of Oral Medicine and Diagnostic Science, College of Dentistry, King Saud University, Riyadh, Saudi Arabia
| | - Omar Youssef
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland.,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.,Faculty of Dentistry, University of Misurata, Misurata, Libya
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Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. FRONTIERS IN ORAL HEALTH 2022; 2:794248. [PMID: 35088057 PMCID: PMC8786902 DOI: 10.3389/froh.2021.794248] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A. Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Almangush A, Mäkitie AA, Leivo I. Cellular dissociation: a missing item in the pathology report and histologic grading of oral tongue cancer? Virchows Arch 2022; 480:717-718. [PMID: 35013773 DOI: 10.1007/s00428-022-03270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Alhadi Almangush
- Department of Pathology, University of Helsinki, 3 (P.O. Box 21), N00014, HaartmaninkatuHelsinki, Finland. .,Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland. .,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland. .,Faculty of Dentistry, Misurata University, Misurata, Libya.
| | - Antti A Mäkitie
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.,Department of Otorhinolaryngology, Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.,Turku University Central Hospital, Turku, Finland
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Lingam A, Koppolu P, Akhter F, Afroz M, Tabassum N, Arshed M, Khan T, ElHaddad S. Future trends of artificial intelligence in dentistry. JOURNAL OF NATURE AND SCIENCE OF MEDICINE 2022. [DOI: 10.4103/jnsm.jnsm_2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Choi S, Park J, Park S, Byon I, Choi HY. Establishment of a prediction tool for ocular trauma patients with machine learning algorithm. Int J Ophthalmol 2021; 14:1941-1949. [PMID: 34926212 DOI: 10.18240/ijo.2021.12.20] [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: 06/21/2021] [Accepted: 10/22/2021] [Indexed: 11/23/2022] Open
Abstract
AIM To predict final visual acuity and analyze significant factors influencing open globe injury prognosis. METHODS Prediction models were built using a supervised classification algorithm from Microsoft Azure Machine Learning Studio. The best algorithm was selected to analyze the predicted final visual acuity. We retrospectively reviewed the data of 171 patients with open globe injury who visited the Pusan National University Hospital between January 2010 and July 2020. We then applied cross-validation, the permutation feature importance method, and the synthetic minority over-sampling technique to enhance tool performance. RESULTS The two-class boosted decision tree model showed the best predictive performance. The accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve were 0.925, 0.962, 0.833, 0.893, and 0.971, respectively. To increase the efficiency and efficacy of the prognostic tool, the top 14 features were finally selected using the permutation feature importance method: (listed in the order of importance) retinal detachment, location of laceration, initial visual acuity, iris damage, surgeon, past history, size of the scleral laceration, vitreous hemorrhage, trauma characteristics, age, corneal injury, primary diagnosis, wound location, and lid laceration. CONCLUSION Here we devise a highly accurate model to predict the final visual acuity of patients with open globe injury. This tool is useful and easily accessible to doctors and patients, reducing the socioeconomic burden. With further multicenter verification using larger datasets and external validation, we expect this model to become useful worldwide.
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Affiliation(s)
- Seungkwon Choi
- Department of Ophthalmology, Pusan National University Hospital, Busan 49241, Republic of Korea.,Biomedical Research Institute of Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Jungyul Park
- Department of Ophthalmology, Pusan National University Hospital, Busan 49241, Republic of Korea.,Biomedical Research Institute of Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Sungwho Park
- Department of Ophthalmology, Pusan National University Hospital, Busan 49241, Republic of Korea.,Biomedical Research Institute of Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Iksoo Byon
- Department of Ophthalmology, Pusan National University Hospital, Busan 49241, Republic of Korea.,Biomedical Research Institute of Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Hee-Young Choi
- Department of Ophthalmology, Pusan National University Hospital, Busan 49241, Republic of Korea.,Biomedical Research Institute of Pusan National University Hospital, Busan 49241, Republic of Korea.,Department of Ophthalmology, School of Medicine, Pusan National University, Busan 49241, Republic of Korea
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Yu B, Huang C, Xu J, Liu S, Guan Y, Li T, Zheng X, Ding J. Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images. BMC Oral Health 2021; 21:585. [PMID: 34798867 PMCID: PMC8603498 DOI: 10.1186/s12903-021-01947-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/03/2021] [Indexed: 12/24/2022] Open
Abstract
Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.
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Affiliation(s)
- Baoting Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China
| | - Shuo Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Yuyao Guan
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Tong Li
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Xuewei Zheng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Jun Ding
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China. .,Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China.
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Shavlokhova V, Sandhu S, Flechtenmacher C, Koveshazi I, Neumeier F, Padrón-Laso V, Jonke Ž, Saravi B, Vollmer M, Vollmer A, Hoffmann J, Engel M, Ristow O, Freudlsperger C. Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study. J Clin Med 2021; 10:5326. [PMID: 34830608 PMCID: PMC8618824 DOI: 10.3390/jcm10225326] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/11/2021] [Accepted: 11/13/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. MATERIAL AND METHODS Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. RESULTS The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. CONCLUSION In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.
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Affiliation(s)
- Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Sameena Sandhu
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | | | | | | | | | - Žan Jonke
- Munich Innovation Labs GmbH, 80336 Munich, Germany; (V.P.-L.); (Ž.J.)
| | - Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, 79106 Freiburg, Germany;
| | - Michael Vollmer
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Andreas Vollmer
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Jürgen Hoffmann
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Michael Engel
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Oliver Ristow
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
| | - Christian Freudlsperger
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany; (S.S.); (M.V.); (A.V.); (J.H.); (M.E.); (O.R.); (C.F.)
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Alabi RO, Hietanen P, Elmusrati M, Youssef O, Almangush A, Mäkitie AA. Mitigating Burnout in an Oncological Unit: A Scoping Review. Front Public Health 2021; 9:677915. [PMID: 34660505 PMCID: PMC8517258 DOI: 10.3389/fpubh.2021.677915] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 08/24/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: The purpose of this study was to provide a scoping review on how to address and mitigate burnout in the profession of clinical oncology. Also, it examines how artificial intelligence (AI) can mitigate burnout in oncology. Methods: We searched Ovid Medline, PubMed, Scopus, and Web of Science, for articles that examine how to address burnout in oncology. Results: A total of 17 studies were found to examine how burnout in oncology can be mitigated. These interventions were either targeted at individuals (oncologists) or organizations where the oncologists work. The organizational interventions include educational (psychosocial and mindfulness-based course), art therapies and entertainment, team-based training, group meetings, motivational package and reward, effective leadership and policy change, and staff support. The individual interventions include equipping the oncologists with adequate training that include-communication skills, well-being and stress management, burnout education, financial independence, relaxation, self-efficacy, resilience, hobby adoption, and work-life balance for the oncologists. Similarly, AI is thought to be poised to offer the potential to mitigate burnout in oncology by enhancing the productivity and performance of the oncologists, reduce the workload and provide job satisfaction, and foster teamwork between the caregivers of patients with cancer. Discussion: Burnout is common among oncologists and can be elicited from different types of situations encountered in the process of caring for patients with cancer. Therefore, for these interventions to achieve the touted benefits, combinatorial strategies that combine other interventions may be viable for mitigating burnout in oncology. With the potential of AI to mitigate burnout, it is important for healthcare providers to facilitate its use in daily clinical practices. Conclusion: These combinatorial interventions can ensure job satisfaction, a supportive working environment, job retention for oncologists, and improved patient care. These interventions could be integrated systematically into routine cancer care for a positive impact on quality care, patient satisfaction, the overall success of the oncological ward, and the health organizations at large.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | | | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Omar Youssef
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland.,University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Ben-Omran MO, Livinski AA, Kopycka-Kedzierawski DT, Boroumand S, Williams D, Weatherspoon DJ, Iafolla TJ, Fontelo P, Dye BA. The use of teledentistry in facilitating oral health for older adults: A scoping review. J Am Dent Assoc 2021; 152:998-1011.e17. [PMID: 34521539 DOI: 10.1016/j.adaj.2021.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 05/13/2021] [Accepted: 06/02/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Teledentistry is used in many countries to provide oral health care services. However, using teledentistry to provide oral health care services for older adults is not well documented. This knowledge gap needs to be addressed, especially when accessing a dental clinic is not possible and teledentistry might be the only way for many older adults to receive oral health care services. TYPES OF STUDIES REVIEWED Nine databases were searched and 3,396 studies were screened using established eligibility criteria. Included studies were original research or review articles in which the intervention of interest was delivered to an older adult population (≥ 60 years) via teledentistry. The authors followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review criteria. RESULTS Nineteen studies were identified that met the criteria for inclusion. Only 1 study was from the United States. Seven studies had results focusing on older adult participants only, with most of those conducted in elder care facilities. The remainder consisted of studies with mixed-age populations reporting distinct results or information for older adults. The included studies used teledentistry, in both synchronous and asynchronous modes, to provide services such as diagnosis, oral hygiene promotion, assessment and referral of oral emergencies, and postintervention follow-up. CONCLUSIONS AND PRACTICAL IMPLICATIONS Teledentistry comprises a variety of promising apps. The authors identified and described uses, promising possibilities, and limitations of teledentistry to improve the oral health of older adults.
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Adeoye J, Tan JY, Choi SW, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021; 154:104557. [PMID: 34455119 DOI: 10.1016/j.ijmedinf.2021.104557] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.
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Affiliation(s)
- John Adeoye
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jia Yan Tan
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Siu-Wai Choi
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Peter Thomson
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
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Almangush A, Alabi RO, Mäkitie AA, Leivo I. Machine learning in head and neck cancer: Importance of a web-based prognostic tool for improved decision making. Oral Oncol 2021; 124:105452. [PMID: 34266743 DOI: 10.1016/j.oraloncology.2021.105452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Institute of Biomedicine, Pathology, University of Turku, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya.
| | - Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland; Department of Pathology, Turku University Hospital, Turku, Finland
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Parimbelli E, Wilk S, Cornet R, Sniatala P, Sniatala K, Glaser SLC, Fraterman I, Boekhout AH, Ottaviano M, Peleg M. A review of AI and Data Science support for cancer management. Artif Intell Med 2021; 117:102111. [PMID: 34127240 DOI: 10.1016/j.artmed.2021.102111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/23/2020] [Accepted: 05/11/2021] [Indexed: 02/09/2023]
Abstract
INTRODUCTION Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.
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Affiliation(s)
| | - S Wilk
- Poznan University of Technology, Poland
| | - R Cornet
- Amsterdam University Medical Centre, the Netherlands
| | | | | | - S L C Glaser
- Amsterdam University Medical Centre, the Netherlands
| | - I Fraterman
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - A H Boekhout
- Netherlands Cancer Institute, Amsterdam, the Netherlands
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Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021; 115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. OBJECTIVES This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. DATA SOURCES We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. ELIGIBILITY CRITERIA Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. DATA EXTRACTION Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. RESULTS A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. CONCLUSION Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
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Chen Y, Huang S, Chen T, Liang D, Yang J, Zeng C, Li X, Xie G, Liu Z. Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare. Am J Nephrol 2021; 52:152-160. [PMID: 33744876 DOI: 10.1159/000513566] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/01/2020] [Indexed: 01/20/2023]
Abstract
BACKGROUND Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare. METHODS We randomly divided 1,694 patients with biopsy-proven LN, who had achieved remission after treatment, into a derivation cohort (n = 1,186) and an internal validation cohort (n = 508), at a ratio of 7:3. The risk of renal flare 5 years after remission was predicted using an eXtreme Gradient Boosting (XGBoost) method model, developed from 59 variables, including demographic, clinical, immunological, pathological, and therapeutic characteristics. A simplified risk score prediction model (SRSPM) was developed from important variables selected by XGBoost model using stepwise Cox regression for practical convenience. RESULTS The 5-year relapse rates were 39.5% and 38.2% in the derivation and internal validation cohorts, respectively. Both the XGBoost model and the SRSPM had good predictive performance, with a C-index of 0.819 (95% confidence interval [CI]: 0.774-0.857) and 0.746 (95% CI: 0.697-0.795), respectively, in the validation cohort. The SRSPM comprised 6 variables, including partial remission and endocapillary hypercellularity at baseline, age, serum Alb, anti-dsDNA, and serum complement C3 at the point of remission. Using Kaplan-Meier analysis, the SRSPM identified significant risk stratification for renal flares (p < 0.001). CONCLUSIONS Renal flare of LN can be readily predicted using the XGBoost model and the SRSPM, and the SRSPM can also stratify flare risk. Both models are useful for clinical decision-making and individualized management in LN.
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Affiliation(s)
- Yinghua Chen
- National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Siwan Huang
- Ping An Healthcare Technology, Beijing, China
| | - Tiange Chen
- Ping An Healthcare Technology, Beijing, China
| | - Dandan Liang
- National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Jing Yang
- National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Caihong Zeng
- National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Xiang Li
- Ping An Healthcare Technology, Beijing, China
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing, China
- Ping An Health Cloud Co. Limited, Beijing, China
- Ping An International Smart City Technology Co, Beijing, China
| | - ZhiHong Liu
- National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China,
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Alhazmi A, Alhazmi Y, Makrami A, Masmali A, Salawi N, Masmali K, Patil S. Application of artificial intelligence and machine learning for prediction of oral cancer risk. J Oral Pathol Med 2021; 50:444-450. [PMID: 33394536 DOI: 10.1111/jop.13157] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/21/2020] [Accepted: 12/29/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic-pathological features. METHODS A popular data mining algorithm artificial neural network was used for developing the artificial intelligence-based prediction model. A total of 29 variables that were associated with the patients were used for developing the model. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. All records and observations were reviewed by Board-certified oral pathologist. RESULTS A total of 73 patients met the eligibility criteria. Twenty-two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. Thirty-seven were female, and 36 were male, with a mean age of 63.09 years. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10-fold cross-validation analysis was 85.71% (95% confidence interval [CI], 57.19-98.22) and 60.00% (95% CI, 14.66-94.73), respectively. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43-931.95). CONCLUSION Our results suggest that this machine-learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic-pathological data.
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Affiliation(s)
- Anwar Alhazmi
- Department of Preventive Dental Science, Jazan University, College of Dentistry, Jazan, Saudi Arabia
| | - Yaser Alhazmi
- Department of Maxillofacial Surgery and Diagnostic Sciences, Jazan University, College of Dentistry, Jazan, Saudi Arabia
| | - Ali Makrami
- Prince Mohammed Bin Nasser Hospital, Ministry of health, Jazan, Saudi Arabia
| | | | | | | | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Jazan University, College of Dentistry, Jazan, Saudi Arabia
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Almangush A, Mäkitie AA, Leivo I. Back to basics: Hematoxylin and eosin staining is the principal tool for histopathological risk assessment of oral cancer. Oral Oncol 2020; 115:105134. [PMID: 33384250 DOI: 10.1016/j.oraloncology.2020.105134] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 12/01/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Institute of Biomedicine, Pathology, University of Turku, Turku, Finland; Faculty of Dentistry, University of Misurata, Misurata, Libya.
| | - Antti A Mäkitie
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska Hospital, Stockholm, Sweden.
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.
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Perineural invasion in head and neck squamous cell carcinoma: background, mechanisms, and prognostic implications. Curr Opin Otolaryngol Head Neck Surg 2020; 28:90-95. [PMID: 32011398 DOI: 10.1097/moo.0000000000000610] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW Perineural invasion (PNI) is a pathological feature frequently observed in head and neck squamous cell carcinoma (HNSCC). The difficulties of pathological standardization and the lack of a simple validated experimental model to study PNI render its analysis complex. Here, we aim to summarize the recent advances in the understanding of the biology of PNI in HNSCC and their potential clinical implications. RECENT FINDINGS PNI is a multistep process leading to a dialogue between cancer cells and nerve fibers in HNSCC. Recent studies have identified some of the active molecular mechanisms involved in PNI. Comprehensive studies addressing the transcriptional regulation of PNI bring interesting perspectives for a standardized molecular diagnosis of PNI and a better assessment of its contribution to the aggressiveness of HNSCC. SUMMARY Perineural invasion is a complex process that reflects specific tumor biology. In addition to unveiling new fundamental concepts about the tumor microenvironment, research on PNI promises to identify new biomarkers, enabling progress in therapeutic development against HNSCC.
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Alabi RO, Mäkitie AA, Pirinen M, Elmusrati M, Leivo I, Almangush A. Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer. Int J Med Inform 2020; 145:104313. [PMID: 33142259 DOI: 10.1016/j.ijmedinf.2020.104313] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/04/2020] [Accepted: 10/20/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. OBJECTIVES This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population. METHODS The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver-operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients. RESULTS The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival. CONCLUSION The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, University of Misurata, Misurata, Libya
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Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors. Med Sci (Basel) 2020; 8:medsci8040042. [PMID: 33036481 PMCID: PMC7712376 DOI: 10.3390/medsci8040042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/06/2020] [Accepted: 09/21/2020] [Indexed: 11/22/2022] Open
Abstract
(1) Background: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery or the improvement in the preoperative radiological assessment, facial nerve injury (FNI) continues to be the most feared complication; (2) Methods: patients who underwent parotid gland surgery for benign tumors between June 2010 and June 2019 were included in this study aiming to make a proof of concept about the reliability of an artificial neural networks (AAN) algorithm for prediction of FNI and compared with a multivariate linear regression (MLR); (3) Results: Concerning prediction accuracy and performance, the ANN achieved the highest sensitivity (86.53% vs 46.23%), specificity (95.67% vs 92.59%), PPV (87.28% vs 66.94%), NPV (95.68% vs 83.37%), ROC–AUC (0.960 vs 0.769) and accuracy (93.42 vs 80.42) than MLR; and (4) Conclusions: ANN prediction models can be useful for otolaryngologists—head and neck surgeons—and patients to provide evidence-based predictions about the risk of FNI. As an advantage, the possibility to develop a calculator using clinical, radiological and histological or cytological information can improve our ability to generate patients counselling before surgery.
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Kumar S, Patnaik S, Dixit A. Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC). PeerJ 2020; 8:e9656. [PMID: 33024622 PMCID: PMC7518185 DOI: 10.7717/peerj.9656] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/14/2020] [Indexed: 01/02/2023] Open
Abstract
Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.
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Affiliation(s)
- Sugandh Kumar
- Computational Biology and Bioinformatics Laboratory, Institute of Life Science, Bhubaneswar, Odisha, India.,School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha, India
| | - Srinivas Patnaik
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odisha, India
| | - Anshuman Dixit
- Computational Biology and Bioinformatics Laboratory, Institute of Life Science, Bhubaneswar, Odisha, India
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