Basal Cell Carcinoma of SkinNCT07682831
Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
A study testing Two photon microscopy imaging in people with Basal Cell Carcinoma of Skin and Squamous Cell Carcinoma (Skin), run by University of Rochester at 1 site in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Basal Cell Carcinoma of Skin, Squamous Cell Carcinoma (Skin)
- What is being tested?
- Two photon microscopy imaging
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 92 participants
- Where
- 1 siteUnited States
- Study dates
- 24 Jun 2026to 1 Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Punch, excisional or shave biopsy specimen
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Rochester Dermatologic Surgery
Victor, New York, 14654
Sherrif Ibrahim, M.D.-Ph.D., 585-222-1400, dr.ibrahim@rochesterdermsurgery.com
About this study
Official title: Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
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?
Read the 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.
- Being tested
- Two photon microscopy imaging
- Keywords
- two-photon microscopy, machine learning
- Medical subject headings
- Carcinoma, Basal Cell, Carcinoma, Squamous Cell
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Two photon microscopy imagingDevice
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
| Study group | Role | What they receive |
|---|---|---|
| TPFM imaging of biopsy | Experimental | Specimens will be imaged with TPFM and diagnosed using a machine learning model Receives: Device: Two photon microscopy imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Sensitivity will be calculated for the machine learning model using two photon fluorescence microscopy images. Sensitivity is defined as the number of true positive diagnoses divided by the sum of true positive and false negative diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
Measured: During or immediately following patient biopsy (same day)
Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Specificity will be calculated for the machine learning model using two photon fluorescence microscopy images. Specificity is defined as the number of true negative diagnoses divided by the sum of true negative and false positive diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
Measured: During or immediately following patient biopsy (same day)
Secondary outcomes (2)
Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors
For biopsy specimens with discordant diagnoses between the machine learning model and the patient's ultimate clinical diagnosis, a dermatopathologist will review each case and classify the source of disagreement as machine learning model interpretation error, image quality limitation, or image coregistration error. The proportion of discordant diagnoses attributable to each source of disagreement will be reported.
Measured: After completion of patient diagnosis (typically 1-2 weeks after procedure)
Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis
The proportion of biopsy specimens for which the machine learning model provides a definitive diagnosis based on two photon fluorescence microscopy images will be calculated as the number of specimens receiving a definitive diagnosis divided by the total number of specimens evaluated.
Measured: During or immediately following patient biopsy (same day)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start24 Jun 2026
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Basal Cell Carcinoma of SkinStudies of the same condition, open and under way.
- More trials run by University of RochesterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 92 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Rochester
- Sponsor type
- University, hospital or other
- Collaborators
- National Cancer Institute (NCI), Rochester Dermatologic Surgery
- Responsible party
- Principal investigator, Michael Giacomelli, University of Rochester
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07682831
- Sponsor's protocol ID
- STUDY00009823B
- Other IDs
- R37CA258376
- Status last verified
- Aug 2026
- First submitted
- 25 Jun 2026
- Last updated
- 19 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: Deidentified sets of two-photon images and corresponding conventional histology will be made available upon request. Links to full resolution image data will be included in publications along with the results of machine learning analysis.
Study at a glance
From the registry record, last updated by the sponsor on 19 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
- ClinicalTrials.gov ID
- NCT07682831
- Acronym / sponsor ID
- STUDY00009823B
- Keywords
- two-photon microscopy, machine learning
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 92 participants (estimated)
- Lead sponsor
- University of RochesterUniversity, hospital or other
- Collaborators
- National Cancer Institute (NCI), Rochester Dermatologic Surgery
- Study start
- 24 Jun 2026 (actual)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Jul 2027 (estimated)
- First posted
- 6 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| TPFM imaging of biopsy | Experimental | Device: Two photon microscopy imagingSpecimens will be imaged with TPFM and diagnosed using a machine learning model |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Punch, excisional or shave biopsy specimen
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Two photon microscopy imagingEx vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis | Device | TPFM imaging of biopsy |
Primary outcome
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)
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Sensitivity will be calculated for the machine learning model using two photon fluorescence microscopy images. Sensitivity is defined as the number of true positive diagnoses divided by the sum of true positive and false negative diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
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)
A machine learning model will evaluate TPFM images of patient biopsies at point of care. Specificity will be calculated for the machine learning model using two photon fluorescence microscopy images. Specificity is defined as the number of true negative diagnoses divided by the sum of true negative and false positive diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.
2 secondary outcomes
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Aug 2026.
- Individual participant dataSharing planned: Yes. Deidentified sets of two-photon images and corresponding conventional histology will be made available upon request. Links to full resolution image data will be included in publications along with the results of machine learning analysis.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Two photon microscopy imaging in people with Basal Cell Carcinoma of Skin and Squamous Cell Carcinoma (Skin), run by University of Rochester at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Michael Giacomelli, Ph.D (5852766260, mgiacome@ur.rochester.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Aug 2026.