Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
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
- The protocol lists: Two photon microscopy imaging.
- Who it may be relevant to
- Registry conditions: Basal Cell Carcinoma of Skin, Squamous Cell Carcinoma (Skin). Basic parameters: No limits · 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
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
Overview
The goal of this study is to investigate the ability of a machine learning model to evaluate two-photon fluorescence microscopy images of dermatologic biopsies at point of care. The main question it aims to answer is: • How well do two-photon fluorescence images of biopsies taken in a clinic and evaluated by a machine learning model agree with conventional histology?
Detailed description
This study will image biopsy specimens at point of care using two-photon fluorescence microscopy (TPFM) and then assess how well the images predict the eventual clinical diagnosis using a machine learning model. Because two-photon images can be acquired from small biopsy specimens within minutes of excision, they could potentially be used to immediately diagnose patients, but the accuracy of TPFM for various skin conditions is unknown.
Individual biopsy specimens in a dermatology clinic will be imaged using TPFM shortly after biopsy procedures. Immediately following imaging, a machine learning model will evaluate the TPFM images then compute a confidence score for a diagnosis of basal cell carcinoma (BCC), squamous cell carcinoma, and non-cancer. The relative confidence in each diagnosis will be compared, and if sufficient confidence is achieved, the model will render a diagnosis or else flag the specimen as indeterminate for manual pathologist review. This workflow will evaluate the use of ML + TPFM to perform point of care diagnosis of skin lesions.
Following TPFM imaging, the specimen will be submitted for histological processing, which will guide actual patient treatment. Following conclusion of patient treatment, the resulting histology slides will be scanned for comparison and the final patient diagnosis recorded. Images of the histology slides will be read by a pathologist to establish a gold-standard diagnosis. The official diagnosis and the diagnosis from the collaborating pathologist will be compared.
Patient treatment will still be decided by conventional histopathology. TPFM will not be used to change treatment.
Interventions
- Device Two photon microscopy imaging
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
Primary outcome measures
- Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care [Time frame: During or immediately following patient biopsy (same day)]
- Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care [Time frame: During or immediately following patient biopsy (same day)]
Secondary outcome measures (2)
- Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors [Time frame: After completion of patient diagnosis (typically 1-2 weeks after procedure)]
- Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis [Time frame: During or immediately following patient biopsy (same day)]
Eligibility criteria
Inclusion criteria
- Punch, excisional or shave biopsy specimen
Exclusion criteria
- Biopsy indication includes melanoma or dysplastic/atypical nevus
- Excision thickness of less than 1 mm
- Excision longest dimension less than 2 mm
- Excision performed as multiple pieces in a single specimen container
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Diagnostic
Study locations
United States · 1 center
- Rochester Dermatologic Surgery — Victor
Identifiers
NCT: NCT07682831 · STUDY00009823B · R37CA258376