AI-Assisted Endoscopy for Upper Aerodigestive Tract Lesions
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Upper Aerodigestive Tract Neoplasms, Upper AerodigestiveTract Cancer, Upper Aero-digestive Tract (UADT) Neoplasm, Squamous Cell Carcinoma. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Belgium, Italy, Spain
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Head&Neck Application of Novel Computer-assisted Endoscopy
Overview
This is a prospective observational clinical study designed to evaluate the performance of artificial intelligence (AI) algorithms applied to upper aerodigestive tract (UADT) video-endoscopy. The study assesses three main tasks: lesion detection (localization), classification (benign vs malignant), and segmentation of tumor margins. AI algorithms will be applied to endoscopic video data acquired during routine clinical practice without influencing clinical decision-making. The system will process images in real time and store data for subsequent analysis. AI outputs will be compared with physician assessment and reference standard histopathology to evaluate diagnostic performance.
Detailed description
The artificial intelligence algorithms developed will be employed in the analysis of laryngeal lesions for 3 tasks:
* Task 1: Computer aided diagnosis (CADx): the algorithm provides a differential diagnosis between benign and malignant neoplasms (binary classification) and the exact histology (multiclass classification). During the UADT video-endoscopy in the outpatient clinic, the physician performs the video-endoscopy and selects and captures n.3 WL and n.3 NBI significant frames of the lesion. The AI model records the classification output of the algorithm that the physician cannot access. The predicted pathologic results will be finally displayed as two different classifications along with the probability of each prediction (0% to 100%) as estimated by the AI algorithm: a first binary classification "neoplastic" or "non-neoplastic," and a second multiclass classification with the exact histology. The physician subsequently, based on the endoscopic examination, will write the suspected diagnosis (benign vs. malignant lesion and the actual histology) in the appropriate patient chart. Next, the physician reviews the screenshot taken and makes sure the lesion is visible in every one of them. Retrospectively, an investigator (blinded to the physician's assessment) will review the AI processed frames with the resulting CADx classifications and mark the AI-processed diagnosis in the patient chart. Once biopsied, the final histology of the lesion analyzed by definitive histopathological examination is recorded in the patient chart by the investigator. The investigators will finally compare the two recorded diagnoses (CADx and physician) with the definitive histology. * Task 2: Computer aided detection (CADe): the algorithm, through the representation of a rectangle (bounding box), localizes the lesion during the video-endoscopy in the outpatient clinic in real-time. During the UADT video-endoscopy, the physician performs the video-endoscopy as for standard-of-care procedure. In parallel, the AI model processes in real-time the endoscopic video and records the output of the algorithm (which the physician cannot access). The physician captures n.3 WL and n.3 NBI significant frames of the lesion. Moreover, n.3 frames where no lesions are visible are captured as negative controls. Later, the physician reviews the screenshot taken and makes sure to label the frames where the lesion is visible as "positive cases" and the frame where the lesion is not visible as "negative cases". The investigators will finally assess if the lesion was detected by the CADe system to define a "true positive". Similarly, to define a true negative, the CADe system should have not output a bounding box in the majority of the "negative cases" frames. * Task 3: Computer aided segmentation (CASe): the algorithm analyzes the neoplasm margins and provides a delineation mask. In the operating room setting, once the lesion to be resected is identified with a 0° telescope, the surgeon captures n.1 WL and n.1 NBI close-up photographs that exemplify the superficial lesion margins. The same procedure is repeated with a 70° optics and other two photographs are acquired. The frames taken are then saved and analyzed by the AI algorithm, which will perform the segmentation task. The surgeon will be blinded to the AI prediction. Later, the surgeon will draw the margins of the lesion according to her/his evaluation of each captured frame. The annotated frame will be saved so that it can be analyzed at a later time. Afterwards, in cases where positive superficial margins are identified by histopathologic examination, the surgeon-designed margins and the AI model ones will be compared to see if there was any difference in the suggested margin.
Primary outcome measures
- Negative Predictive Value of the CADx Algorithm for Malignant or Premalignant Upper Aerodigestive Tract Lesions [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- Sensitivity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions [Time frame: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.]
- Median Intersection Over Union Between CASe Segmentation and Surgeon-Drawn Lesion Margins [Time frame: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.]
- Median Dice Similarity Coefficient Between CASe Segmentation and Surgeon-Drawn Lesion Margins [Time frame: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.]
Secondary outcome measures (11)
- WL-NPV vs. NBI-NPV of CADx classification [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- Clinician-Reported Usability Score for the AI Endoscopy System [Time frame: Assessed after clinician use of the AI system during study procedures, up to 20 months after study initiation.]
- Sensitivity, Specificity and Accuracy of CADx histology prediction [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- F1 Score of CADx Classification [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- Area Under the Receiver Operating Characteristic Curve of CADx Classification [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- Sensitivity, Specificity and Accuracy of human physician histology prediction [Time frame: From index outpatient UADT video-endoscopy until definitive histopathology result is available, assessed up to 60 days after endoscopy.]
- Specificity of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions [Time frame: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.]
- Accuracy of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions [Time frame: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.]
- Positive Predictive Value of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions [Time frame: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.]
- Negative Predictive Value of the CADe Algorithm for Localization of Upper Aerodigestive Tract Lesions [Time frame: At index outpatient UADT video-endoscopy, with blinded post-processing assessment performed up to 30 days after endoscopy.]
- Percentage of Positive Superficial Margin Cases in Which the AI-Predicted Tumor Area Is Wider Than the Surgeon-Drawn Area [Time frame: At intraoperative pre-resection endoscopy, with assessment performed after image annotation up to 30 days after surgery.]
Eligibility criteria
Inclusion criteria
- Age > 18 years
- Injury originating from the upper aero-digestive tract
- Recording of the video-endoscopic examination
- Patient known to undergo a biopsy of the lesion or clinical follow-up for lesion with known biopsy (e.g. laryngeal papillomatosis) or suffering from Reinke's edema (in this pathology, in fact, biopsy is not necessary since the diagnosis is clinical)
- Or patients undergoing transoral lesion excision
Exclusion criteria
- Submucosal lesion
- Patients with previous operations on the upper aero-digestive tract
- Patients with previous radiotherapy of the head and neck district
- Poor compliance on endoscopic examination
- Unavailability of CADe/CADx or CASe data logging note
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Belgium · 1 center
- UZ Leuven — Leuven
Italy · 1 center
- IRCCS Ospedale Policlinico San Martino — Genova
Spain · 1 center
- Hospital Clínic de Barcelona — Barcelona
Publications
- Dunham ME, Kong KA, McWhorter AJ, Adkins LK. Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network. Laryngoscope. 2022 Feb;132 Suppl 4:S1-S8. doi: 10.1002/lary.28708. Epub 2020 Apr 28. PMID 32343434
- Piazza C, Peretti G, Vander Poorten V. Editorial: Advances in Transoral Approaches for Laryngeal Cancer. Front Oncol. 2018 Oct 17;8:455. doi: 10.3389/fonc.2018.00455. eCollection 2018. No abstract available. PMID 30386742
- Paderno A, Piazza C, Del Bon F, Lancini D, Tanagli S, Deganello A, Peretti G, De Momi E, Patrini I, Ruperti M, Mattos LS, Moccia S. Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective. Front Oncol. 2021 Mar 24;11:626602. doi: 10.3389/fonc.2021.626602. eCollection 2021. PMID 33842330
- Azam MA, Sampieri C, Ioppi A, Africano S, Vallin A, Mocellin D, Fragale M, Guastini L, Moccia S, Piazza C, Mattos LS, Peretti G. Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection. Laryngoscope. 2022 Sep;132(9):1798-1806. doi: 10.1002/lary.29960. Epub 2021 Nov 25. PMID 34821396
- Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q, Ma L, Sun Y, Xu W, Yang N, Zou J, Zheng Y, Chen M, Gan W, Xiang T, An J, Liu R, Lv C, Lin K, Zheng X, Lou F, Rao Y, Yang H, Liu K, Liu G, Lu T, Zheng X, Zhao Y. Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020 Nov;130(11):E686-E693. doi: 10.1002/lary.28539. Epub 2020 Feb 18. PMID 32068890
- Kim DH, Kim Y, Kim SW, Hwang SH. Use of narrowband imaging for the diagnosis and screening of laryngeal cancer: A systematic review and meta-analysis. Head Neck. 2020 Sep;42(9):2635-2643. doi: 10.1002/hed.26186. Epub 2020 May 4. PMID 32364313
- Rex DK, Kahi C, O'Brien M, Levin TR, Pohl H, Rastogi A, Burgart L, Imperiale T, Ladabaum U, Cohen J, Lieberman DA. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2011 Mar;73(3):419-22. doi: 10.1016/j.gie.2011.01.023. PMID 21353837
Identifiers
NCT: NCT07596355 · IIT_AIRCARE_H&NANCE