Neoplasms

Symptoms and General Pathology

All Conditions

Skin and Connective Tissue Diseases

Eye Diseases

Rare Diseases

Neoplasm Metastasis

Melanoma

Uveal Neoplasms

Neuroendocrine Tumors

Neuroectodermal Tumors

Neuroectodermal Tumors, Primitive

Neoplasms, Germ Cell and Embryonal

Neoplasms by Histologic Type

Neoplasms, Nerve Tissue

Nevus, Pigmented

Nevus

Nevi and Melanomas

Skin Neoplasms

Skin Diseases

Eye Neoplasms

Uveal Diseases

Intraocular Melanoma

Neuroendocrine Tumor

Neuroepithelioma

Patients diagnosed with uveal melanoma undergoing histological and cytogenetic analysis. The cases will be identified from patients referred to the Ocular Oncology Unit and they finished the follow-up

Patients diagnosed with uveal melanoma undergoing histological and cytogenetic analysis. The cases will be identified from patients referred to the Ocular Oncology and they still have to complete the follow-up

Array-CGH methods on oligonucleotide platforms from Agilent Technologies were used. Tumour DNA from MU samples was extracted using the Gentra Pure Gene commercial kit (QIAGEN), as per clinical practice. Once extracted, the quantity and purity of the DNA obtained were evaluated. All samples deemed suitable for quality were processed following the protocol indicated by Agilent Technologies. The data obtained were analysed using Cytogenomics software. These analyses will allow a genotypic characterisation of the tumour to be obtained.

cytogenetic analysis on tumour tissue

Digital Pathology is an emerging discipline that enables quantitative analysis of digital images using highly standardised approaches. It will allow quantitative measurements of the interactions between immune cells and characterise their relative spatial distribution. In addition, it will be able to generate further information that will allow in-depth study of the tumour microenvironment of uveal melanoma

Digital Pathology

Monica Maria Pagliara, MD,PhD

Monica Maria Pagliara, MD

The objectives of the study are the morpho-phenotypic evaluation of uveal melanoma and to identify molecular prognostic factors that may be correlated with disease severity, tumour progression and response to treatment.

These objectives will be achieved through immunohistochemical and genetic analyses.

Tissues from surgical specimens regardless of the stage of the disease, fixed in formalin and included in paraffin from samples already collected for clinical practice and kept in the archives of the UOC of Pathological Anatomy of the Fondazione Policlinico Universitario A. Gemelli IRCCS will be used for the study, subject to the patient's informed consent to the use of these samples for research purposes. The UOC Anatomia Patologica will take care of the recovery of archive material (slides).

The pseudoanonymised slides, which will be prepared by clinical practice, will be sent to the Institute of Pathological Histology and Molecular Diagnostics of the Azienda Ospedaliero- Universitaria Careggi, Florence for immunohistochemical analysis, which will be carried out as reported below. Once the following analysis has been performed, the slides will be destroyed.

Sections of 3 μm thickness derived from FFPE samples will be prepared for immunohistochemical analysis. Sample processing will be performed using an automated immunostainer (Ventana Discovery ULTRA, Ventana Medical Systems, Tucson, AZ). Sections will be deparaffinised in EZ prep (#950-102; Ventana Medical Systems, Tucson, AZ), followed by treatment with Cell Conditioning 1 (CC1) buffer (#950-124; Ventana Medical Systems, Tucson, AZ) in order to promote antigenic recovery. Finally, primary antibodies will be dispensed according to the desired staining and the signal will be developed with Ultramap anti-mouse or anti-rabbit detection kits. For TME characterisation, samples will be stained with the following primary antibodies: CD3, CD4, CD8, CD68, CD163. All sections processed with IHC will be digitally scanned in Whole Slide Images (WSI) using the Aperio AT2 platform (Leica Biosystems, Wetzlar, DE).Digital Pathology, which will be performed at the University of Florence, is an emerging discipline that allows the quantitative analysis of digital images using highly standardised approaches. It will allow quantitative measurements of the interactions between immune cells and characterise their relative spatial distribution. In addition, it will be able to generate further information that will allow the TME of the MU to be studied in depth.

The HALO image analysis platform, present at the University of Florence, will allow: i) cell quantification, evaluation of the intensity of the marking, recognition of cell compartments (nucleus, cytoplasm, membrane); ii) spatial analysis through the identification of the relative distributions of cells in the intra- and peri-tumour portion; iii) sharing of the database generated together with the digital images.

Samples labelled with single or multiple IHC will be evaluated using this automated digital quantification system in order to characterise the various cell subsets that make up the TME. By combining multiple immunohistochemistry with digital analysis, the data will be accurately analysed, thus producing a new analysis work-flow for finding new signatures. All data derived from the digital analysis will be correlated with the progression of the pathology, with the aim of identifying new prognostic factors in the pathology of MU.

The analysis for the identification of cytogenetic alterations, whose data are already available to us since they are regularly performed in clinical practice, was conducted on the entire cohort of patients enrolled for the study at the Genetics Institute of the Fondazione Policlinico Gemelli on fresh tissue samples taken in the operating theatre as per clinical practice. Array-CGH methods on oligonucleotide platforms from Agilent Technologies were used. Tumour DNA from MU samples was extracted using the Gentra Pure Gene commercial kit (QIAGEN) as per clinical practice; once extracted, the quantity and purity of the DNA obtained was evaluated. All samples deemed suitable for quality were processed following the protocol indicated by Agilent Technologies. The data obtained were analysed using Cytogenomics software. These analyses will allow a genotypic characterisation of the tumour to be obtained.

Morphological, Genetic and Tumour Microenvironment Characterisation in Uveal Melanoma

Morphological, Genetic and Tumour Microenvironment Characterisation in Uveal Melanoma and Impact on the Development of Metastasis and Mortality.

The primary objective of the study is the morpho-phenotypic evaluation of uveal melanoma.

identify molecular prognostic factors that may correlate with disease severity, tumour progression and response to treatment.

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Principal Investigator

Blood and Lymph Conditions

Recurrence

Carcinoma

Thymoma

Thymus Neoplasms

Neoplasms, Glandular and Epithelial

Thoracic Neoplasms

Lymphatic Diseases

Thymic Epithelial Tumor

Patients diagnosed with the following TET subtypes:

* Thymoma Type A
* Thymoma Type AB
* Thymoma Type B1
* Thymoma Type B2
* Thymoma Type B3
* Thymic Carcinoma

Patients with thymic epithelial tumors who have experienced recurrence.

AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.

Artificial Intelligence Diagnostics

This AI tool evaluates thymic tumour data and other clinical data and calculates the risk of recurrence, with the aim of analysing whether there is an association with specific subtypes of thymic epithelial tumours and clinical data.

Recurrence Prediction Tool

Anna Salut Esteve Domínguez

Thymic epithelial tumors are rare neoplasms in the anterior mediastinum. The cornerstone of the treatment is surgical resection. Administration of postoperative radiotherapy is usually indicated in patients with more extensive local disease, incomplete resection and/or more aggressive subtypes, defined by the WHO histopathological classification.

In this classification thymoma types A, AB, B1, B2, B3, and thymic carcinoma are distinguished. Studies have shown large discordances between pathologists in subtyping these tumors. Moreover, the WHO classification alone does not accurately predict the risk of recurrence, as within subtypes patients have divergent prognoses.

The investigators will develop AI models using digital pathology and relevant clinical variables to improve the accuracy of histopathological classification of thymic epithelial tumors, and to better predict the risk of recurrence.

In this multicentric and international project three existing databases will be used from Rotterdam, Maastricht and Lyon. For all models one database will be used to build AI models, and the other two for external validation.

The ultimate goal of this project is to develop AI models that support the pathologist in correctly subtyping thymic epithelial tumors, in order to prevent patients from under- or overtreatment with adjuvant radiotherapy.

Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors

Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors

AI-models 1-3 will be built and validated on the EMC-database, while AI-model 4 will be built on the MUMC+-database and validated on both. Model performance will be assessed using sensitivity, specificity, negative/positive predictive value. Decision analysis curves will quantify the clinical benefit, identifying patient groups with the largest utility.

The EMC-dataset includes 179 TET-patients classified by experienced TET-pathologists. Cases with good agreement between pathologists will be used for training AI-models. Evaluation includes digitized pathology slides assessed by an international expert-panel. The MUMC-database (137 patients) and CHUL-database (181 patients) provide additional data, including clinical variables. Relevant factors include age, gender, tumor volume, stage, completeness of resection, autoimmune disorders, and treatment details.

This outcome aims to create an AI-framework with two principal goals. First, investigate TET-subtypes using four different models emphasizing cell type, morphological structures, and a combination. Second, classify patients based on recurrence outcome within 5 years. An ablation study will be conducted with state-of-the-art deep learning classifiers (ResNet, Inception).

Maastro Clinic, The Netherlands

Hospices Civils de Lyon

Erasmus Medical Center

PhD student

Breast Neoplasms

Breast Diseases

QPlasia OncoReader Breast (QPORB)

Nicolas M Orsi, BSc MBChB PhD

Elizabeth M Walsh, BSc MBChB

The goal of this non-interventionist observational study is to test the performance of a computer algorithm (QPORB) which examines breast cancer biopsy digital images to provide diagnostic support. The main question\[s\] it aims to answer are:

1. The principal research aim is to determine whether 4D Path's Technology Q-Plasia OncoReader Breast, that has been developed in the research setting, works robustly in the clinical environment (i.e. to define its real-life clinical utility) in terms of breast carcinoma grading and molecular subtyping
2. The secondary research aim is to perform an economic analysis alongside the trial in order to establish the time, resource and cost savings that the technology could afford the NHS, the ideal price point for engaging with the technology and the cost:benefit ratio for evaluation by the National Institute for Clinical Excellence
3. The tertiary research aim is to gather long-term follow-up data to better understand long-term response to therapy and prognosis and potential future uses of the algorithm

Participants's specimens will be tested alongside routine clinical workflows without intervention or consent. Researchers will compare the algorithm's results to those of routine diagnostic standard of care workflows.

This study follows on from work conducted over the last 4 years focusing on developing computer-aided diagnostic algorithms in histopathology, with a focus on the detection and typing of breast cancer. The algorithm extracts information from the same digital image of a breast biopsy glass slide as used by pathologists to make a diagnosis. The algorithm can identify the presence of invasive cancer, grade it (i.e. how aggressive it looks) and determine its molecular subtype, all from the same image and in a single step. Whilst a pathologist can identify invasive cancer and grade it on a biopsy specimen, grading is inconsistent across reporting clinicians (i.e. prone to inter-observer variability) and only about 80% aligned with the 'true' grade of the tumour obtained at surgery. Moreover, additional time-consuming and expensive laboratory tests are required for molecular subtyping. They are however essential for informing oncologists of the most effective/targeted therapy for the patient in each case. The aim of this study is to perform a final real-life acid test of the technology in the clinical environment separately but in parallel with routine diagnostic services. If successful, the technology will have acquired a sufficient body of data to convince end-users and regulatory bodies of its merit and will effectively make it ready for deployment in the clinical environment. In that domain, this diagnostic solution health economics assessment will run alongside the clinical evaluation of the algorithm to analyse and compare cost and time-related factors between the routine clinical service and the algorithm workflow. This will provide further information required by regulatory bodies to aid the consideration of utilising the technology within current health systems. Furthermore, follow-up data will be collected at 5 and 10 years to investigate patient prognosis and treatment response as well as future potential applications of the technology.

CLinical Evaluation Of a comPuter Algorithm To Report BreAst cAncers (CLEOPATRAA)

CLinical Evaluation Of a comPuter Algorithm To Report BreAst cAncers

The tertiary research aim is to gather long-term follow-up data to better understand long-term response to therapy and prognosis and potential future uses of the algorithm

The principal research aim is to determine whether 4D Path's Technology Q-Plasia OncoReader Breast, that has been developed in the research setting, works robustly in the clinical environment (i.e. to define its real-life clinical utility) in terms of breast carcinoma grading and molecular subtyping

The secondary research aim is to perform an economic analysis alongside the trial in order to establish the time, resource and cost savings that the technology could afford the NHS, the ideal price point for engaging with the technology and the cost:benefit ratio for evaluation by the National Institute for Clinical Excellence

University of Leeds

4DPath

The Leeds Teaching Hospitals NHS Trust

Mouth and Tooth Diseases

Keratosis

Hyperplasia

Mouth Diseases

Precancerous Conditions

Leukoplakia

Erythroplasia

Stomatognathic Diseases

Erythroplakia

No dysplasia, mild, moderate, severe dysplasia, CIS

Assessment for risk of progression to oral cancer

STRATICYTE™ Test

Jody Filkowski, BSc, MSc, PhD

Jason TK Hwang, HBSc, MSc, PhD

Andrew G Sikora, MD, PhD

Simon W Young, DDS, MD, PhD

The purpose of this study is to validate the ability of the STRATICYTE™ predictive model to predict the transformation of oral potentially malignant disorders (OPMDs) to oral squamous cell carcinoma (OSCC) in a retrospective cohort of patients who received biopsies.

The study objectives are to:

1. Evaluate STRATICYTE™ sensitivity and specificity in a cohort of patients that meet the inclusion/exclusion criteria.
2. Identify patient and clinical characteristics influencing the sensitivity and specificity of the STRATICYTE™ model.
3. Estimate the correlation between STRATICYTE™ outcome and time to a positive diagnosis of oral cancer.

FFPE specimens immunohistochemically stained with S100A7

Evaluation of Oral Potentially Malignant Disorders (OPMDs) With STRATICYTE™

Standard measures of accuracy calculated using cross-tabulation of predicted risk category and confirmed cancer progression over a 5-year period from biopsy

Kaplan-Meier analyses to compare time to a positive diagnosis of oral cancer between patients/biopsies with a STRATICYTE™ classification of Low- and Elevated-Risk.

From all possible pairs of patients, compute the number of concordant and discordant pairs. Compute C-index as the proportion of all possible pairs that are concordant.

The University of Texas Health Science Center, Houston

Loma Linda University

Minnesota Oral & Facial Surgery

University of Alabama at Birmingham

Proteocyte Diagnostics Inc.

Respiratory Tract (Lung and Bronchial) Diseases

Adenocarcinoma

Lung Neoplasms

Adenocarcinoma of Lung

Respiratory Tract Neoplasms

Lung Diseases

Respiratory Tract Diseases

Lung Adenocarcinoma

Patients with a presumptive diagnosis of lung cancer for whom surgical resection or sampling will be clinically indicated according to the standard practices of the Cardiothoracic Department of AHEPA University Hospital of Thessaloniki, will be enrolled in this prospective study once they give written informed consent for specimen imaging. Surgical resections will be performed per standard of care and there will be no difference in patients' clinical management depending on the acquisition or not of surgical specimens. Patients with altered mental status and those who are unable or unwilling to provide informed consent will be excluded from this study.

Micro-computed tomography will be utilized to scan surgically resected lung tissue specimens.

micro-computed tomography scanning of lung tissue specimens

Andreas S Papazoglou, MD

Dimitrios V Moysidis, MD

Micro-computed tomography (micro-CT) is a novel biomedical non-destructive, slide-free digital imaging modality, which enables the rapid acquisition of accurate high-resolution, volumetric images of intact surgical tissue specimens. This imaging modality provides microscopic level of detail of intact tissues in three-dimensions without requiring any specimen preparation. Its non-destructive nature and the ongoing enhancement of imaging resolution and contrast renders micro-CT imaging particularly well suited for microanatomic studies in basic research across a wide range of interventional medical disciplines, including oncology.

Our proposal concerns a multidisciplinary basic research effort which aims to facilitate the effective identification of different -and maybe challenging to differentiate- lung cancer patterns based on 3D X-ray histology. As an alternative for the use of hematoxylin \& eosin (H\&E) slides, optimized micro-CT scanning of soft tissues emerges as a promising tool to enable non-invasive 3D X-ray histology of formalin-fixed and paraffin-embedded (FFPE) lung cancer specimens.

The objective of our proposal is to offer novel insights into the complex architecture of each lung cancer subtype after imaging FFPE surgical specimens, resected from lung cancer surgeries. The investigators aim to generate 3D datasets of FFPE lung cancer tissues which will be combined with the corresponding conventional 2D histology slides. Our study will be also adequately empowered to identify particular differences in morphometric measurements according to each particular lung cancer growth pattern.

Finally, this proposal aims to delineate the different 3D microanatomy and morphology of some patterns that are challenging to interpret and differentiate through traditional 2D histological evaluation, such as papillary and lepidic adenocarcinoma growth patterns.

Classification of the histological subtypes based on 2D histology sections can be ambiguous, as shown by suboptimal inter-observer consensus when determining predominant histological subtypes in FFPE lung adenocarcinoma tissue specimens. Hence, micro-CT-based 3D imaging of the lung specimens could aid classification of histological subtypes by providing more comprehensive sampling of the entire tissue block and yielding detail relevant for subtype classification that might not be visible in 2D sections alone.

Patients with a presumptive diagnosis of lung cancer for whom surgical resection or sampling will be clinically indicated according to the standard practices of the Cardiothoracic Department of AHEPA University Hospital of Thessaloniki, will be enrolled in this prospective study once they give written informed consent for specimen imaging. Surgical resections will be performed per standard of care and there will be no difference in patients' clinical management depending on the acquisition or not of surgical specimens. Patients with altered mental status and those who are unable or unwilling to provide informed consent will be excluded from this study.

Sample preparation Following surgical resection, human lung tissue specimens will be placed in a sterile container by the surgeon in the operating room. Choice of container will be based on specimen size. Right after the end of the operation, each specimen will be fixed in neutral buffered formalin for 48 hours in tissue cassettes and will be embedded in paraffin wax following a standardized protocol.

Imaging protocol After the collection of the formalin-fixed and paraffin-embedded (FFPE) surgically resected lung cancer specimens, these will be transported to the μ-VIS X-ray Imaging Centre at the University of Southampton in accordance with particular biological material transfer agreement. The FFPE lung samples will be scanned at the μ-VIS X-ray Imaging Centre using a custom-built Nikon Metrology micro-CT scanner according to standardized protocol.

Histological assessment Following non-destructive micro-CT imaging, the scanned FFPE specimens will be transported to the Pathology Laboratory of the First Pathology Department of the National and Kapodistrian University of Athens in accordance with particular biological material transfer agreement. The specimens will be set in paraffin blocks for sectioning. After sectioning, sections will be de-paraffinized and stained using Movat's pentachrome stain. The sections will be imaged and assessed histologically by an experienced and "blinded" pulmonary pathologist (S.T.). The pathologist will also assess a number of non-scanned FFPE lung cancer specimens to confirm the non-destructive nature of micro-CT imaging for the resected specimens. Identification of radiation-induced or ischemic alterations, deviant necrosis or cellular degeneration could be indicative outcomes of a destructive imaging method.

Statistical analysis The generated imaging data will be statistically analyzed using Bland-Altman plots to determine intra- and inter-observer variability in the interpretation of micro-CT imaging data. Wilcoxon's rank sum test or Kruskal Wallis's test will be utilized to evaluate any differences within specific morphometric measurements (percent object volume, object surface/volume ratio, object surface density, structure model index, structure thickness, structure linear density, structure separation, connectivity, connectivity density) among different growth patterns. All statistical analyses will be performed with SPSS (version 27) and a p-value of less than 0.05 will be considered as the threshold of statistical significance.

Lung tissue specimens will be surgically resected according to clinical indications (per standard of care) in patients with presumptive clinical diagnosis of lung cancer.

Prognostic Value of Lung Cancer MicroAnatomy in 3D

Prognostic Value of Lung Cancer MicroAnatomy in Three Dimensions: a Micro-CT Based Trial

After co-registration of micro-CT and histopathological images via relevant software (e.g., ImageJ plugin: UnwarpJ elastic registration), a blinded pathologist (S.T.) will assess the co-aligned images to assess whether the investigator can identify the presence of lung cancer based on the generated 3D micro-CT images. The outcomes of this qualitative analysis will be statistically analyzed using Bland-Altman plots to determine intra- and inter-observer variability in the interpretation of micro-CT imaging data.

Wilcoxon's rank sum test or Kruskal Wallis's test will be utilized to evaluate any differences within specific morphometric micro-CT based measurements among different growth patterns. All statistical analyses will be performed with SPSS (version 27) and a p-value of less than 0.05 will be considered as the threshold of statistical significance.

National and Kapodistrian University of Athens

University of Southampton

Aristotle University Of Thessaloniki

Anti-Infective Agents

Central Nervous System Depressants

All Drugs and Chemicals

Herbal and Botanical

Ethanol

Orange

EUS FNA/B for solid pancreatic lesions. After the FNA/FNB, the needle was removed from the EUS endoscope and the specimen obtained after the first needle pass was expelled and placed directly in a dedicated scaffold (Cytomatrix, UCS Diagnostics).

a drop of ethanol was put on it and the liquid was drained away thanks to the porous structure of the matrix. This step was followed by the deposition of a drop of acridine-orange for 20" and a rapid washing with buffered saline. After that, the Cytomatrix was put on a dedicated microscopic slide and covered with a second slide before the introduction in the slot of the machine.

The FCM VivaScope® 2500 (2500M-G4; VivaScope, Munich, Germany) was used for immediate imaging.

Endoscopic ultrasound fine needle biopsy

Endoscopic ultrasound-guided (EUS) tissue acquisition is the current standard of care for the diagnosis of pancreatic solid lesions but it is burdened by a non-negligible risk of non-diagnostic or inconclusive results.

Ex-vivo fluorescence confocal laser microscopy (FCM) with MAVIG VivaScope® 2500M-G4 could allow real time assessment of adequacy and diagnosis of the sample.

Endoscopic ultrasound-guided (EUS) tissue acquisition is the current standard of care for the diagnosis of pancreatic solid lesions.

However, EUS-sampling is burdened by a non-negligible risk of non-diagnostic or inconclusive results, with an estimated negative predictive value not higher than 70-80%.

ROSE could improve the diagnostic yeld of this procedure. Data about the utility of rapid on-site evalutation (ROSE) are still controversial. Without ROSE, current guidelines suggest performance of three to four needle passes with an fine needle aspiration (FNA) needle or two to three passes with a fine needle biopsy (FNB) needle.

Potential drawbacks of ROSE are the considerable time commitment, costs and logistical and personnel issues that arise because of the "on demand" nature of these procedures with the need to have available technologists.

Ex-vivo fluorescence confocal laser microscopy (FCM) is an optical technology for rapid microscopic digital imaging of fresh unfixed biological specimens without any slide preparation.

FCM involves no damage or cell/tissue loss during the examination and the sample can subsequently be recovered and formalin-fixed and paraffin-embedded (FFPE) for permanent histology. FCM allows both cellular and architectural details to be visualized, similar to standard histological analysis of paraffin-embedded or frozen samples. With FCM, multimodal confocal microscopy using different laser sources and fusion images can generate optical plans similar to those from hematoxylin/eosin (H\&E)- stained sections directly from native tissues.

Recently, the feasibility of FCM with MAVIG VivaScope® 2500M-G4 in EUS-FNB sample of pancreatic solid lesions has been investigated . This previous single-center study demonstrated good concordance between the FCM image-based evaluation and the final FFPE histology.

However in this first study, a single type of FNB needle was used for all the procedures and the pathologist involved was expert in digital pathology and in pancreatic disease. Therefore it might be not representative of the everyday clinical practice. In addition the first study was mainly focused on the adequacy assessment of the samples.

Digital Confocal Microscopy for Real-time Diagnosis of Pancreatic Solid Lesion

Digital Confocal Microscopy for Real-time Diagnosis of Pancreatic Solid Lesion: a Multicentre Study

To evaluate the accuracy of fluorescence confocal microscopy (FCM) for ex-vivo examination of samples obtained from EUS- FNA/B in pancreatic solid lesions to assess final diagnosis against final histological diagnosis.

Campus Bio-Medico University

Head of Operative Digestive Endoscopy department

Carcinoma, Squamous Cell

Disease Progression

Mouth Neoplasms

Head and Neck Neoplasms

Oral Squamous Cell Carcinoma

Oral Cancer

Lip and Oral Cavity Cancer

No evidence of dysplasia

Mild dysplasia

Moderate dysplasia

Severe dysplasia

Assessment for mild, moderate, or severe dysplasia, and risk of progression to oral cancer

Standard of Care Histopathology

STRATICYTE Assessment

Jason Hwang, PhD

Mark Darling, MSc, MChD

Tim McGaw, DDS, MSc, MD

The purpose of this observational study is to evaluate the utility of the S100A7 immunohistochemistry signature-based assessment - STRATICYTE - in determining the risk of progression to cancer of clinically suspicious oral lesions.

Background: The standard of care for potentially premalignant oral epithelial lesion (PPOEL) risk assessment for progression to cancer is dysplasia grading by histopathology. With significant overlap between dysplasia grades and high inter- and intra-observer variations, dysplasia grading alone has been shown to be inadequate as a prognostic tool. To investigate the utility of the S100A7 immunohistochemistry signature-based assessment - STRATICYTE - in the early diagnosis of invasive oral cancer, a prospective multi-center observational study was designed with specimens obtained from community-based practices.

Methods: Patients that qualify to enroll in the study will be assessed for both standard of care histopathological assessment for dysplasia grade and STRATICYTE risk assessment, and followed for up to 60 months (from initial biopsy) to determine the outcome of their oral lesion(s).

Formalin-fixed paraffin-embedded tissue sections stained with H\&E and S100A7

Early Prediction of Oral Cancer by S100A7 Immunohistochemistry Signature-based Assessment

A Multi-Centre Prospective Community-Based Observational Study on Prediction of Malignant Progression of Clinically Suspicious Oral Lesions With STRATICYTE

Cancer progression rate in patients with oral neoplasia with dysplasia and STRATICYTE Low-Risk or Elevated Risk

Cancer progression rate in patients with oral neoplasia without dysplasia and STRATICYTE Low-Risk or Elevated-Risk

Recurrence rate in patients with oral neoplasia with or without dysplasia and STRATICYTE Low-Risk or Elevated-Risk

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Morphological, Genetic and Tumour Microenvironment Characterisation in Uveal Melanoma	NCT05889481	Uveal Melanoma	cytogenetic ana... Digital Patholo...	18 Years -	Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors	NCT06301945	Thymic Epitheli... Thymic Carcinom... Thymoma Thymoma and Thy...	Artificial Inte... Recurrence Pred...	-	Erasmus Medical Center
CLinical Evaluation Of a comPuter Algorithm To Report BreAst cAncers (CLEOPATRAA)	NCT06110845	Breast Cancer	QPlasia OncoRea...	16 Years - 110 Years	The Leeds Teaching Hospitals NHS Trust
Evaluation of Oral Potentially Malignant Disorders (OPMDs) With STRATICYTE™	NCT05853783	Mouth Diseases	STRATICYTE™ Tes...	18 Years -	Proteocyte Diagnostics Inc.
Prognostic Value of Lung Cancer MicroAnatomy in 3D	NCT05565677	Lung Cancer Adenocarcinoma ... Pathology Micro-computed ... X-ray Histology Digital Patholo...	micro-computed ...	18 Years -	Aristotle University Of Thessaloniki
Digital Confocal Microscopy for Real-time Diagnosis of Pancreatic Solid Lesion	NCT06315439	Pancreas Adenoc... Endoscopic Ultr... Digital Patholo...	Endoscopic ultr...	18 Years - 90 Years	Campus Bio-Medico University
Morphological, Genetic and Tumour Microenvironment Characterisation in Uveal Melanoma	NCT05889481	Uveal Melanoma	cytogenetic ana... Digital Patholo...	18 Years -	Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Digital Confocal Microscopy for Real-time Diagnosis of Pancreatic Solid Lesion	NCT06315439	Pancreas Adenoc... Endoscopic Ultr... Digital Patholo...	Endoscopic ultr...	18 Years - 90 Years	Campus Bio-Medico University
Morphological, Genetic and Tumour Microenvironment Characterisation in Uveal Melanoma	NCT05889481	Uveal Melanoma	cytogenetic ana... Digital Patholo...	18 Years -	Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Early Prediction of Oral Cancer by S100A7 Immunohistochemistry Signature-based Assessment	NCT04622462	Oral Neoplasm	Standard of Car... STRATICYTE Asse...	15 Years - 90 Years	Proteocyte Diagnostics Inc.

Spots Global Cancer Trial Database for digital pathology