Intra-operative point-of-procedure delineation of oral cancer margins using optical coherence tomography.

ObjectivesSurgical margin status is a significant determinant of treatment outcome in oral cancer. Negative surgical margins can decrease the loco-regional recurrence by five-fold. The current standard of care of intraoperative clinical examination supplemented by histological frozen section, can result in a risk of positive margins from 5 to 17 percent. In this study, we attempted to assess the utility of intraoperative optical coherence tomography (OCT) imaging with automated diagnostic algorithm to improve on the current method of clinical evaluation of surgical margin in oral cancer.Materials and methodsWe have used a modified handheld OCT device with automated algorithm based diagnostic platform for imaging. Intraoperatively, images of 125 sites were captured from multiple zones around the tumor of oral cancer patients (n = 14) and compared with the clinical and pathologic diagnosis.ResultsOCT showed sensitivity and specificity of 100%, equivalent to histological diagnosis (kappa, ĸ = 0.922), in detection of malignancy within tumor and tumor margin areas. In comparison, for dysplastic lesions, OCT-based detection showed a sensitivity of 92.5% and specificity of 68.8% and a moderate concordance with histopathology diagnosis (ĸ = 0.59). Additionally, the OCT scores could significantly differentiate squamous cell carcinoma (SCC) from dysplastic lesions (mild/moderate/severe; p ≤ 0.005) as well as the latter from the non-dysplastic lesions (p ≤ 0.05).ConclusionThe current challenges associated with clinical examination-based margin assessment could be improved with intra-operative OCT imaging. OCT is capable of identifying microscopic tumor at the surgical margins and demonstrated the feasibility of mapping of field cancerization around the tumor

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

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

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Development of deep learning methods for head and neck cancer detection in hyperspectral imaging and digital pathology for surgical guidance

Surgeons performing routine cancer resections utilize palpation and visual inspection, along with time-consuming microscopic tissue analysis, to ensure removal of cancer. Despite this, inadequate surgical cancer margins are reported for up to 10-20% of head and neck squamous cell carcinoma (SCC) operations. There exists a need for surgical guidance with optical imaging to ensure complete cancer resection in the operating room. The objective of this dissertation is to evaluate hyperspectral imaging (HSI) as a non-contact, label-free optical imaging modality to provide intraoperative diagnostic information. For comparison of different optical methods, autofluorescence, RGB composite images synthesized from HSI, and two fluorescent dyes are also acquired and investigated for head and neck cancer detection. A novel and comprehensive dataset was obtained of 585 excised tissue specimens from 204 patients undergoing routine head and neck cancer surgeries. The first aim was to use SCC tissue specimens to determine the potential of HSI for surgical guidance in the challenging task of head and neck SCC detection. It is hypothesized that HSI could reduce time and provide quantitative cancer predictions. State-of-the-art deep learning algorithms were developed for SCC detection in 102 patients and compared to other optical methods. HSI detected SCC with a median AUC score of 85%, and several anatomical locations demonstrated good SCC detection, such as the larynx, oropharynx, hypopharynx, and nasal cavity. To understand the ability of HSI for SCC detection, the most important spectral features were calculated and correlated with known cancer physiology signals, notably oxygenated and deoxygenated hemoglobin. The second aim was to evaluate HSI for tumor detection in thyroid and salivary glands, and RGB images were synthesized using the spectral response curves of the human eye for comparison. Using deep learning, HSI detected thyroid tumors with 86% average AUC score, which outperformed fluorescent dyes and autofluorescence, but HSI-synthesized RGB imagery performed with 90% AUC score. The last aim was to develop deep learning algorithms for head and neck cancer detection in hundreds of digitized histology slides. Slides containing SCC or thyroid carcinoma can be distinguished from normal slides with 94% and 99% AUC scores, respectively, and SCC and thyroid carcinoma can be localized within whole-slide images with 92% and 95% AUC scores, respectively. In conclusion, the outcomes of this thesis work demonstrate that HSI and deep learning methods could aid surgeons and pathologists in detecting head and neck cancers.Ph.D

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review

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

Artificial Intelligence-based methods in head and neck cancer diagnosis : an overview

Background This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. Methods Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009–2020). No restrictions were placed on the AI/ML method or imaging modality used. Results In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). Conclusions There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice

Organotypic head and neck cancer models for advanced preclinical drug testing

Head and neck squamous cell carcinoma (HNSCC) reflect a highly heterogeneous and aggressive group of cancers for which concurrently many therapy options are associated with adverse side effects and resistance mechanisms. Although intense research is performed, the five-year survival rate is stagnating and remains low, with 50% on average. Preclinical drug testing is mainly based on animal testing, even though the gap between the species is known and the results are often misleading. Since tissue-engineered human-based models get more and more attention, due to their promising results in reflecting the human in vivo situation closely, models of normal oral mucosa (NOM) and tumor oral mucosa (TOM) have been developed in this thesis for advanced preclinical drug testing to improve HNSCC therapy. Hereby, NOM models reflected lining mucosa, with a defined basal membrane, the stratum basale, and stratum spinosum and primary tumor cells from patient-derived xenografts (PDX) and tumor cell lines could be integrated into the models by reflecting their original tumor grading-status. To elongate the model´s cultivation time, the commonly used collagen in the model´s matrix was replaced by a tight-knit web of esterified hyaluronic acid fibers, called Hyalograft 3D®. The development of the epithelium occurred slower but offered a continuous proliferation of up to 7 weeks in culture, in contrast to the 2 weeks limited functionality in the collagen-based models. This shows the high influence and importance of a well-defined extracellular matrix (ECM) for improved 3D-modeling. Drug effects have been investigated based on docetaxel and cetuximab, which are frequently used against head and neck squamous cell carcinoma, by comparing systemic and topical application routes. Docetaxel presented its potency by tumor mass reduction, with increased cell damage and inflammation as detected by lactate dehydrogenase and interleukin-6 release into the medium. Furthermore, a reduced proliferation (Ki-67), angiogenesis (HIF-1), and increased apoptosis (TUNEL) could be determined. Interestingly, the topical application often needed less docetaxel dosage to achieve the same cytostatic effects, compared to systemic application. In a first proof-of-concept study UHPLC-MS/MS analysis was integrated into the models, to enable automated sampling for docetaxel-concentrations inside the tumor tissue. Since sample preparations are dropped, this approach seems promising for future pharmacokinetic investigations. In contrast to docetaxel, cetuximab did not inhibit the proliferation of the tumor cells. Since cetuximab frequently triggers tumor resistances, it first had to be guaranteed, that the drug reaches its target site. Therefore, in cooperation with the physical institute of Freie Universität Berlin, the fluorescence-lifetime imaging microscopy and the atomic force microscopy-based infrared spectroscopy served for analysis. In summary, the established models could improve preclinical drug testing since the models closely reflect the human in vivo situation, are easily adaptable, and offer various drug-testings, be it based on morphology, pharmacokinetics, or drug detection. Future minimization of the models might allow high-throughput analysis and approaches for personalized medicine. Moreover, the integration of immune and blood cells could enable the study of a wider drug range and reflect the in vivo situation even more detailed. My developed and analyzed NOM and TOM models promise improved preclinical drug testing and promote the principles of 3R as the reduction, replacement, and refinement of animal testing.Plattenepithelkarzinome im Kopf- und Halsbereich stellen eine sehr heterogene und aggressive Krebsart dar, bei der derzeitige Therapieoptionen mit starken Nebenwirkungen und Resistenzmechanismen assoziiert sind. Obwohl intensiv Forschung betrieben wird, stagniert die 5-Jahresüberlebensrate und verbleibt niedrig, mit 50% im Durchschnitt. Die präklinische Wirkstofftestung basiert hauptsächlich auf Tierversuchen, obwohl die Kluft zwischen den Spezies bekannt ist und die Ergebnisse meistens irreführend sind. Da die Züchtung von humanen Gewebsmodellen immer mehr Aufmerksamkeit erhält, auf Grund ihrer vielversprechenden Ergebnisse, die humane in vivo Situation nahe widerzuspiegeln, wurden Modelle der normalen Mundschleimhaut und von Tumormundschleimhaut in dieser Arbeit entwickelt um die präklinische Wirkstofftestung der Kopf- und Halstumortherapie zu verbessern. Hierbei bildeten die normalen Mundschleimhautodelle die auskleidende Mundschleimhaut mit einer definierten Basalmembran, dem Stratum basale und dem Stratum spinosum, ab. Primäre Tumorzellen aus Patienten-generierten Xenotransplantaten und Tumorzelllinien konnten in die Modelle integriert werden und deren ursprüngliche Tumor-klassifizierung wiederspiegeln. Zur Verlängerung der Kultivierungszeit wurde das gewöhnlich verwendete Kollagen in der Modellmatrix durch ein engmaschiges Gewebe aus veresterten Hyaluronsäurefasern, Hyalograft 3D® genannt, ersetzt. Die Ausbildung des Epithels erfolgte hierbei langsamer, gewährte aber eine kontinuierliche Proliferation über bis zu 7 Wochen in Kultur, im Gegensatz zu der auf 2 Wochen beschränkten Funktionalität von Kollagen-basierten Modellen. Dies zeigt den großen Einfluss und die Wichtigkeit einer gut definierten extrazellulären Matrix für verbesserte 3D-Modellierung. Wirkstoffeffekte wurden anhand von Docetaxel und Cetuximab untersucht, die häufig gegen Kopf- und Halskarzinomen eingesetzt werden, indem systemische und topische Applikationen miteinander verglichen wurden. Docetaxel zeigte seine Wirksamkeit durch eine reduzierte Tumormasse, mit erhöhtem Zelluntergang und Entzündungsreaktionen, die durch freigesetzte Laktat Dehydrogenase und Interleukin-6 im Medium detektiert wurden. Weiter konnte eine reduzierte Proliferation (Ki-67) und Angiogenese (HIF-1) und erhöhte Apoptose (TUNEL) Rate festgestellt werden. Interessanterweise wurde bei topischer Applikation oft eine geringere Dosis an Docetaxel benötigt, um dieselben zytostatischen Effekte zu erzielen wie bei systemischer Gabe. Um pharmakokinetische Untersuchungen an den Tumormundschleimhautmodellen zu ermöglichen, wurde weiter ein automatischer Probenzug für die UHPLC-MS/MS Analyse in die Modelle integriert und Docetaxel-Konzentrationen im Tumorgewebe über 5 Tage gemessen. Da Probenaufbereitungen entfielen, erscheint dieser Ansatz erfolgversprechend für zukünftige pharmakokinetische Untersuchungen. Anders als Docetaxel wirkte Cetuximab nicht proliferationsinhibierend auf die Tumorzellen. Da unter Cetuximab häufig Tumorresistenzen auftreten, musste zunächst gewährleistet werden, dass der Arzneistoff im Testmodell zur Zielstruktur gelangt. Hierfür dienten, die Fluoreszenzlebensdauer- Mikroskopie und Rasterkraftmikroskopie-gekoppelte Infrarotspektroskopie. Zusammenfassend könnten die etablierten Modelle die präklinische Wirkstofftestung verbessern, da sie die humane in vivo Situation nahe widerspiegeln, sie leicht adaptiert werden können und für unterschiedlichste Wirkstofftestungen verwendet werden können, sei es im Zuge der Morphologie, Pharmakokinetik oder Wirkstoffdetektion. Zukünftige Minimierung der Modelle könnte weiterhin Hochdurchsatzanalysen und Ansätze für personalisierte Medizin ermöglichen. Weiter könnte die Integrierung von Immun- und Blutzellen Untersuchungen von weiteren Wirkstoffklassen und eine noch detailliertere in vivo Situation Abbildung bewerkstelligen. Meine hier entwickelten normalen Mundschleimhaut und Tumor- Mundschleimhaut Modelle stellen vielversprechende präklinische Testmodelle dar, welche die 3R Prinzipien begünstigt, welche die Vermeidung, Verringerung und Verbesserung von Tierversuchen beinhaltet

Novel Diagnostic Tools for Skin and Periorbital Cancer - Exploring Photoacoustic Imaging and Diffuse Reflectance Spectroscopy

The eyelids are susceptible to a number of skin cancers which are challenging to excise radically without sacrificing excessive healthy tissue. The way in which a tumor is delineated preoperatively has not changed significantly over the past century. The aims of the work presented in this thesis were to investigate two novel non-invasive techniques for diagnosing and delineating skin tumors.Extended-wavelength diffuse reflectance spectroscopy (EWDRS) was evaluated to determine its ability to differentiate between and classify different skin and tissue types in an in vivo pig model, with the aid of machine learning methods.The recordings were used to train a support vector machine, and it was possible to perform classifications with an overall accuracy of over 98%. The ability of EWDRS to identify the borders of pigmented skin lesions in an in vivo pig model was also evaluated. Using a thin probe, it was possible to detect the border with a median discrepancy of 70 μm, compared to the border found on histological examination.Photoacoustic imaging (PAI), a biomedical imaging modality that combines laser irradiation and ultrasound, was used to examine basal cell carcinomas (BCCs) and human eyelids ex vivo. Typical photoacoustic spectra were observed for BCCs as well as for the different layers of the healthy eyelid, and these structures could be visualized in three-dimensional images. A case was described in which PAI showed that the pentagonal excision of an eyelid BCC was non-radical, as was later confirmed by histological examination.In conclusion, both EWDRS and PAI are capable of differentiating between different kinds of tissue and, following further development and studies, could potentially be used to diagnose and delineate skin and eyelid tumors prior to surgical excision

Multimodal Multispectral Optical Endoscopic Imaging for Biomedical Applications

Optical imaging is an emerging field of clinical diagnostics that can address the growing medical need for early cancer detection and diagnosis. Various human cancers are amenable to better prognosis and patient survival if found and treated during early disease onset. Besides providing wide-field, macroscopic diagnostic information similar to existing clinical imaging techniques, optical imaging modalities have the added advantage of microscopic, high resolution cellular-level imaging from in vivo tissues in real time. This comprehensive imaging approach to cancer detection and the possibility of performing an ‘optical biopsy’ without tissue removal has led to growing interest in the field with numerous techniques under investigation. Three optical techniques are discussed in this thesis, namely multispectral fluorescence imaging (MFI), hyperspectral reflectance imaging (HRI) and fluorescence confocal endomicroscopy (FCE). MFI and HRI are novel endoscopic imaging-based extensions of single point detection techniques, such as laser induced fluorescence spectroscopy and diffuse reflectance spectroscopy. This results in the acquisition of spectral data in an intuitive imaging format that allows for quantitative evaluation of tissue disease states. We demonstrate MFI and HRI on fluorophores, tissue phantoms and ex vivo tissues and present the results as an RGB colour image for more intuitive assessment. This follows dimensionality reduction of the acquired spectral data with a fixed-reference isomap diagnostic algorithm to extract only the most meaningful data parameters. FCE is a probe-based point imaging technique offering confocal detection in vivo with almost histology-grade images. We perform FCE imaging on chemotherapy-treated in vitro human ovarian cancer cells, ex vivo human cancer tissues and photosensitiser-treated in vivo murine tumours to show the enhanced detection capabilities of the technique. Finally, the three modalities are applied in combination to demonstrate an optical viewfinder approach as a possible minimally-invasive imaging method for early cancer detection and diagnosis