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- Alabi, RO, et al.
(author)
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Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication
- 2022
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In: International journal of environmental research and public health. - : MDPI AG. - 1660-4601. ; 19:14
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Journal article (peer-reviewed)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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- Mohamed, H, et al.
(author)
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The expression and prognostic value of stem cell markers Bmi-1, HESC5:3, and HES77 in human papillomavirus-positive and -negative oropharyngeal squamous cell carcinoma
- 2019
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In: Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. - : IOS Press. - 1423-0380. ; 41:3, s. 1010428319840473-
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Journal article (peer-reviewed)abstract
- Human papillomavirus is detected in over 50% of oropharyngeal squamous cell carcinomas. Human papillomavirus–positive oropharyngeal squamous cell carcinomas differ from human papillomavirus–negative tumors, and both expression patterns are classified as distinct entities. The Bmi-1 oncogene is a well-known member of the mammalian polycomb-group family. HESC5:3 and HES77 are newly developed monoclonal antibodies produced against undifferentiated embryonic stem cells. Our aim was to explore their roles in both human papillomavirus–positive and –negative oropharyngeal squamous cell carcinomas. Our cohort comprised 202 consecutive oropharyngeal squamous cell carcinoma patients diagnosed and treated with curative intent. We used tissue microarray tumor blocks to study the immunohistochemical expression of Bmi-1, HESC5:3, and HES77. We compared the expressions of these stem cell markers with p16 immunoexpression and human papillomavirus status, as well as with other characteristics of the tumor, and with patients’ clinical data and follow-up data. Human papillomavirus– and p16-positive tumors expressed less Bmi-1 and more HESC5:3 than the negative tumors. HES77 expression was high in human papillomavirus–positive oropharyngeal squamous cell carcinoma, but it did not correlate with p16 positivity. In our multivariable model, Bmi-1 and HESC5:3 were still associated with human papillomavirus, but the association between human papillomavirus and HES77 remained absent. In conclusion, Bmi-1, HESC5:3, and HES77 may have a different role in human papillomavirus–positive and human papillomavirus–negative tumors. There was no correlation between Bmi-1, HESC5:3, and HES77 expression and survival.
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- Svard, F, et al.
(author)
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The risk of second primary cancer after nasopharyngeal cancer: a systematic review
- 2023
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In: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery. - 1434-4726. ; 280:1011, s. 4775-4781
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Journal article (peer-reviewed)
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- Alabi, RO, et al.
(author)
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Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine
- 2021
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In: Frontiers in oral health. - : Frontiers Media SA. - 2673-4842. ; 2, s. 794248-
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Journal article (peer-reviewed)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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- Alabi, RO, et al.
(author)
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Mitigating Burnout in an Oncological Unit: A Scoping Review
- 2021
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In: Frontiers in public health. - : Frontiers Media SA. - 2296-2565. ; 9, s. 677915-
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Journal article (peer-reviewed)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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| 29. |
- Alabi, RO, et al.
(author)
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Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review
- 2021
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In: Frontiers in oral health. - : Frontiers Media SA. - 2673-4842. ; 2, s. 686863-
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Journal article (other academic/artistic)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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- Almangush, A, et al.
(author)
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Biomarkers for Immunotherapy of Oral Squamous Cell Carcinoma: Current Status and Challenges
- 2021
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In: Frontiers in oncology. - : Frontiers Media SA. - 2234-943X. ; 11, s. 616629-
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Journal article (peer-reviewed)abstract
- Oral squamous cell carcinoma (OSCC) forms a major health problem in many countries. For several decades the management of OSCC consisted of surgery with or without radiotherapy or chemoradiotherapy. Aiming to increase survival rate, recent research has underlined the significance of harnessing the immune response in treatment of many cancers. The promising finding of checkpoint inhibitors as a weapon for targeting metastatic melanoma was a key event in the development of immunotherapy. Furthermore, clinical trials have recently proven inhibitor of PD-1 for treatment of recurrent/metastatic head and neck cancer. However, some challenges (including patient selection) are presented in the era of immunotherapy. In this mini-review we discuss the emergence of immunotherapy for OSCC and the recently introduced biomarkers of this therapeutic strategy. Immune biomarkers and their prognostic perspectives for selecting patients who may benefit from immunotherapy are addressed. In addition, possible use of such biomarkers to assess the response to this new treatment modality of OSCC will also be discussed.
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| 39. |
- Almangush, A, et al.
(author)
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Improving Risk Stratification of Early Oral Tongue Cancer with TNM-Immune (TNM-I) Staging System
- 2021
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In: Cancers. - : MDPI AG. - 2072-6694. ; 13:13
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Journal article (peer-reviewed)abstract
- Although patients with early-stage oral tongue squamous cell carcinoma (OTSCC) show better survival than those with advanced disease, there is still a number of early-stage cases who will suffer from recurrence, cancer-related mortality and worse overall survival. Incorporation of an immune descriptive factor in the staging system can aid in improving risk assessment of early OTSCC. A total of 290 cases of early-stage OTSCC re-classified according to the American Joint Committee on Cancer (AJCC 8) staging were included in this study. Scores of tumor-infiltrating lymphocytes (TILs) were divided as low or high and incorporated in TNM AJCC 8 to form our proposed TNM-Immune system. Using AJCC 8, there were no significant differences in survival between T1 and T2 tumors (p > 0.05). Our proposed TNM-Immune staging system allowed for significant discrimination in risk between tumors of T1N0M0-Immune vs. T2N0M0-Immune. The latter associated with a worse overall survival with hazard ratio (HR) of 2.87 (95% CI 1.92–4.28; p < 0.001); HR of 2.41 (95% CI 1.26–4.60; p = 0.008) for disease-specific survival; and HR of 1.97 (95% CI 1.13–3.43; p = 0.017) for disease-free survival. The TNM-Immune staging system showed a powerful ability to identify cases with worse survival. The immune response is an important player which can be assessed by evaluating TILs, and it can be implemented in the staging criteria of early OTSCC. TNM-Immune staging forms a step towards a more personalized classification of early OTSCC.
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- Almangush, A, et al.
(author)
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Stromal categorization in early oral tongue cancer
- 2021
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In: Virchows Archiv : an international journal of pathology. - : Springer Science and Business Media LLC. - 1432-2307. ; 478:5, s. 925-932
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Journal article (peer-reviewed)abstract
- Stromal categorization has been used to classify many epithelial cancer types. We assessed the desmoplastic reaction and compared its significance with other stromal characteristics in early (cT1-2N0) oral tongue squamous cell carcinoma (OTSCC). In this multi-institutional study, we included 308 cases treated for early OTSCC at five Finnish university hospitals or at the A.C. Camargo Cancer Center in São Paulo, Brazil. The desmoplastic reaction was classified as immature, intermediate, or mature based on the amount of hyalinized keloid-like collagen and myxoid stroma. We compared the prognostic value of the desmoplastic reaction with a stromal grading system based on tumor-stroma ratio and stromal tumor-infiltrating lymphocytes. We found that a high amount of stroma with a weak infiltration of lymphocytes was associated statistically significantly with a worse disease-free survival with a hazard ratio (HR) of 2.68 (95% CI 1.26–5.69), worse overall survival (HR 2.95, 95% CI 1.69–5.15), and poor disease-specific survival (HR 2.66, 95% CI 1.11–6.33). Tumors having a high amount of stroma with a weak infiltration of lymphocytes were also significantly associated with a high rate of local recurrence (HR 4.13, 95% CI 1.67–10.24), but no significant association was found with lymph node metastasis (HR 1.27, 95% CI 0.37–4.35). Categorization of the stroma based on desmoplastic reaction (immature, intermediate, mature) showed a low prognostic value for early OTSCC in all survival analyses (P > 0.05). In conclusion, categorization of the stroma based on the amount of stroma and its infiltrating lymphocytes shows clinical relevance in early OTSCC superior to categorization based on the maturity of stroma.
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