Histopathological predictors of early stage oral tongue cancer

Histopathological predictors of early stage oral tongue cancer Oral tongue cancer constitutes the majority of malignancies of the oral cavity. In Finland during 2013, the age-adjusted incidence rates of oral tongue cancer were 1.7 per 100,000 in males and 0.8 per 100,000 in females. Staging of the clinical status of tumor size, lymph node, and metastasis (cTNM staging) is a widely accepted system for classification of many cancers including oral squamous cell carcinoma (OSCC). However, this staging system failed to prognosticate the outcome of early stage oral tongue squamous cell carcinoma (OTSCC). Numerous studies designed to introduce prognostic parameters and/or models to complement the insufficiency of cTNM staging and to predict the patient outcome have been carried out. Many of these prognostic models (or systems) were based on histopathologic parameters. However, all of the previously introduced models showed little or no predictive power in early stage (cT1-2N0) OTSCC. Thus, the identification of markers that predict the outcome of patients with early stage OTSCC is still necessary in order to apply effective individualized treatment. In this international collaborative study, we have examined the prognostic impact of several predictive factors in large patient cohorts from 7 institutions located in 3 geographic regions (Finland, Brazil, and USA). Moreover, we have introduced a new simple predictive model (BD score) for histopathologic classification and prognostication of early OTSCC. We suggest that this model could possibly be easily applied during the surgical resection, so patients with aggressive OTSCC could benefit from elective neck dissection (END) during the same procedure. This model could offer a step forward toward the personalized management of OTSCC. However, additional validation in different patient cohorts is still required.Uusi menetelmä kielisyövän aggressiivisuuden ennustamiseen Kielisyöpä on suun alueen yleisin maligniteetti. Ikävakioitu kielisyövän ilmaantuvuus oli Suomessa vuonna 2013 miehillä 1,7:100000 ja naisilla 0,8:100000. Monet syövät, mukaan lukien suun levyepiteelikarsinoomat luokitellaan yleisesti hyväksytyn nk. cTNM-luokituksen mukaan kasvaimen kliinisen koon (T), imusolmukelöydösten (N) ja metastasoinnin (M) mukaan. Tällä luokituksella ei kuitenkaan pystytä luotettavasti arvioimaan kielen varhaisen vaiheen (cT1-2N0M0) levyepiteelikarsinooman ennustetta. On olemassa lukuisia tutkimuksia, joissa on pyritty täydentämään cTNM-luokitusta taudin aggressiivisuuden ja potilaan hoitotuloksen ennustettavuuden parantamiseksi tarkastelemalla kielisyöpäkudoksen histologisia piirteitä. Nämä histologiset ennustemallit eivät kuitenkaan ole toimineet tyydyttävästi kielen varhaisen vaiheen levyepiteelikarsinoomien kohdalla. Nyt tässä kansainvälisessä (Suomi, Brasilia, USA) monikeskustutkimuksessa olemme analysoineet lukuisia histologia piirteitä laajassa varhaisvaiheen kielisyövän potilasaineistossa. Kehitimme työssä uuden, käytännön histopatologisessa työssä helposti sovellettavan nk. BD-mallin, jossa kasvaimen silmuileva kasvutapa (Budding) ja syvyys (Depth) todettiin piirteiksi, jotka parhaiten ennustavat syövän käyttäytymistä ja tautikuolleisuutta. Tätä BD-menetelmää ehdotamme käytettäväksi kielisyöpäpotilaan hoidon suunnittelussa, jolloin histologisesti runsaasti silmuileva ja syvä kasvain, joka kuitenkin paljain silmin tarkasteltuna vaikuttaa vain pieneltä alkuvaiheen kasvaimelta, tulisi hoitaa samoin kuin laajemmat kielisyövät poistamalla mm. imusolmukkeita kaulalta. BD-menetelmän käyttöä tulee vielä tutkia lisää ennen kuin se mahdollisesti voidaan lisätä kansainvälisiin hoitosuosituksiin

The 8th Edition of the American Joint Committee on Cancer (AJCC8) Staging Manual : any improvement in the prognostication of oral tongue cancer?

Non peer reviewe

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

Non peer reviewe

Insight into Classification and Risk Stratification of Head and Neck Squamous Cell Carcinoma in Era of Emerging Biomarkers with Focus on Histopathologic Parameters

Tumor-node-metastasis (TNM) staging system is the cornerstone for treatment planning of head and neck squamous cell carcinoma (HNSCC). Many prognostic biomarkers have been introduced as modifiers to further improve the TNM classification of HNSCC. Here, we provide an overview on the use of the recent prognostic biomarkers, with a focus on histopathologic parameters, in improving the risk stratification of HNSCC and their application in the next generation of HNSCC staging systems

The budding and depth of invasion model in oral cancer : A systematic review and meta-analysis

Background Tumour budding (B) and depth of invasion (D) have both been reported as promising prognostic markers in oral squamous cell carcinoma (OSCC). This meta-analysis assessed the prognostic value of the tumour budding and depth of invasion combination (BD model) in OSCC. Methods Databases including Ovid MEDLINE, PubMed, Scopus and Web of Science were searched for articles that studied the BD model as a prognosticator in OSCC. PICO search strategy was "In OSCC patients, does BD model have a prognostic power?" We used the reporting recommendations for tumour marker prognostic studies (REMARK) criteria to evaluate the quality of studies eligible for systematic review and meta-analysis. Results Nine studies were relevant as they analysed the BD model for prognostication of OSCC. These studies used either haematoxylin and eosin (HE) or pan-cytokeratin (PCK)-stained resected sections of OSCC. Our meta-analysis showed a significant association of BD model with OSCC disease-free survival (hazard ratio = 2.02; 95% confidence interval = 1.44-2.85). Conclusions The BD model is a simple and reliable prognostic indicator for OSCC. Evaluation of the BD model from HE- or PCK-stained sections could facilitate individualized treatment planning for OSCC patients.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.© 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

Deep Machine Learning for Oral Cancer : From Precise Diagnosis to Precision Medicine

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.© 2022 Alabi, Almangush, Elmusrati and Mäkitie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.fi=vertaisarvioitu|en=peerReviewed

Impact of Astroprincin (FAM171A1) Expression in Oral Tongue Cancer

Astroprincin (APCN, FAM171A1) is a recently characterized transmembrane glycoprotein that is abundant in brain astrocytes and is overexpressed in some tumors. However, the expression and role of APCN is unknown in oral tongue squamous cell carcinoma (OTSCC). Aim of this study was to investigate the expression of APCN in OTSCC tissue samples and to analyze possible association of APCN with clinicopathological features and survival rates. This study included 76 patients treated for OTSCC. Expression of APCN in OTSCC tissue sections was examined by immunohistochemistry with a rabbit polyclonal antibody (MAP346) against APCN. All tumors were scored for intensity and percentage of APCN staining at the superficial, middle, and invasive front areas. High expression of APCN was significantly associated with increased tumor size (P = 0.013) and with OTSCC recurrence (P = 0.026). In this pilot study, we observed that the amount of APCN is associated with the size and recurrence of OTSCC. This finding suggests a role of APCN during OTSCC progression.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