Back to basics : Hematoxylin and eosin staining is the principal tool for histopathological risk assessment of oral cancer

Non peer reviewe

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

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

Risk stratification in oral squamous cell carcinoma using staging of the eighth American Joint Committee on Cancer : Systematic review and meta-analysis

The eighth edition of the American Joint Committee on Cancer (AJCC8) staging manual has major changes in oral squamous cell carcinoma (OSCC). We searched PubMed, OvidMedline, Scopus, and Web of Science for studies that examined the performance of AJCC8 in OSCC. A total of 40 808 patients were included in the studies of our meta-analysis. A hazard ratio (HR) of 1.87 (95%CI 1.78-1.96) was seen for stage II, 2.65 (95%CI 2.51-2.80) for stage III, 3.46 (95%CI 3.31-3.61) for stage IVa, and 7.09 (95%CI 4.85-10.36) for stage IVb. A similar gradual increase in risk was noted for the N classification. For the T classification, however, there was a less clear variation in risk between T3 and T4. AJCC8 provides a good risk stratification for OSCC. Future research should examine the proposals introduced in the published studies to further improve the performance of AJCC8.Peer reviewe

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

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

Biomarkers for Immunotherapy of Oral Squamous Cell Carcinoma: Current Status and Challenges

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

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

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.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Onko nenänielusta löytynyt parillinen sylkirauhanen - päivittyykö ihmisen makroskooppinen anatomia?

Sylkirauhasilla on keskeinen merkitys fysiologisissa toiminnoissa, jotka liittyvät nielemiseen, purentaan, puheen tuottamiseen ja ruuansulatukseen. Ihmisen suurista sylkirauhasista korvasylkirauhanen on kuvattu ajanlaskumme alun aikoihin. Nykyään kolmen suuren parillisen ja lukuisten pienten sylkirauhasten tehtävät, rakenteet ja sijainti tiedetään täsmällisesti. Pieniä sylkirauhasia tai syljentuotantoa nenänielussa ei kuitenkaan yleensä kuvata, vaikka alueelta lähtöisin olevat sylkirauhaskasvaimet tunnetaan. Hollantilainen tutkimusryhmä havaitsi nenänielussa korvatorven aukon läheisyydessä suurena sylkirauhasena pitämänsä parillisen rakenteen potilailla, joita oli molekyylikuvannettu urologisen syövän takia. Nenänielun sylkirauhasiin viittaavat löydökset havaittiin sylkirauhaskudokseen aktiivisesti hakeutuvalla radioaktiivisella lääkeaineella positroniemissiotomografiassa ({PET}). Löydöstä selvitettiin makroskooppisesti ja mikroskooppisesti vainajatutkimuksessa. Siinä ilmeni, että kuvatulla rauhasella on rakenteellisia yhtäläisyyksiä kielenalus- ja pieniin sylkirauhasiin

Does Evaluation of Tumour Budding in Diagnostic Biopsies have a Clinical Relevance? : A Systematic Review

Tumour budding has emerged as a promising prognostic marker in many cancers. We systematically reviewed all studies that evaluated tumour budding in diagnostic biopsies. We conducted a systematic review of PubMed, MEDLINE, Scopus, Web of Science and Cochrane library for all articles that have assessed tumour budding in diagnostic (i.e. pretreatment or pre-operative) biopsies of any tumour type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies and extracted data from the eligible studies. A total of 13 reports comprising 11 cohorts were found to have studied tumour budding in diagnostic biopsies. All these reports showed that evaluation of tumour budding in diagnostic biopsies was easily applicable. A strong association was observed between tumour budding score in diagnostic biopsies and corresponding surgical samples. Evaluation of tumour budding in diagnostic biopsies had a significant prognostic value for lymph node metastasis and patient survival. In all studies, tumour budding was a valuable marker of tumour aggressiveness and can be evaluated in technically satisfactory diagnostic biopsies. Thus, the assessment of tumour budding seems to identify the behaviour of cancer, and therefore to facilitate treatment planning.Peer reviewe

Bilateral Basal Cell Adenocarcinoma of the Parotid Gland: In a Recipient of Kidney Transplant

We report a rare case of bilateral basal cell adenocarcinoma (BcAC) of the parotid gland in a male patient 30 years after kidney transplantation and continuous administration of immunosuppressive therapy. BcAC is a salivary gland malignancy first recognized as a distinct neoplastic entity in WHO classification of salivary gland tumours in 1991. Over 90% of BcACs are detected in the parotid gland. The most important differential diagnosis is basal cell adenoma. Infiltrative growth is the distinguishing feature of BcAC. Administration of immunosuppressive medication to this patient for three decades may have contributed to development of this rare neoplasia. To our knowledge, similar cases of BcAC have not been reported previously