Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
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?
Panoramica dello studio
Stato
Intervento / Trattamento
Descrizione dettagliata
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.
Tipo di studio
Iscrizione (Stimato)
Fase
- Non applicabile
Contatti e Sedi
Contatto studio
- Nome: Michael Giacomelli, Ph.D
- Numero di telefono: 5852766260
- Email: mgiacome@ur.rochester.edu
Luoghi di studio
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New York
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Victor, New York, Stati Uniti, 14654
- Reclutamento
- Rochester Dermatologic Surgery
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Contatto:
- Sherrif Ibrahim, M.D.-Ph.D.
- Numero di telefono: 585-222-1400
- Email: dr.ibrahim@rochesterdermsurgery.com
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
- Bambino
- Adulto
- Adulto più anziano
Accetta volontari sani
Descrizione
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
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
- Scopo principale: Diagnostico
- Assegnazione: N / A
- Modello interventistico: Assegnazione di gruppo singolo
- Mascheramento: Nessuno (etichetta aperta)
Armi e interventi
Gruppo di partecipanti / Arm |
Intervento / Trattamento |
|---|---|
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Sperimentale: TPFM imaging of biopsy
Specimens will be imaged with TPFM and diagnosed using a machine learning model
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Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
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Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
Lasso di tempo: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
Lasso di tempo: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors
Lasso di tempo: After completion of patient diagnosis (typically 1-2 weeks after procedure)
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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.
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After completion of patient diagnosis (typically 1-2 weeks after procedure)
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Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis
Lasso di tempo: During or immediately following patient biopsy (same day)
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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.
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During or immediately following patient biopsy (same day)
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Collaboratori e investigatori
Sponsor
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Completamento primario (Stimato)
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Primo inviato che soddisfa i criteri di controllo qualità
Primo Inserito (Effettivo)
Aggiornamenti dei record di studio
Ultimo aggiornamento pubblicato (Effettivo)
Ultimo aggiornamento inviato che soddisfa i criteri QC
Ultimo verificato
Maggiori informazioni
Termini relativi a questo studio
Parole chiave
Termini MeSH pertinenti aggiuntivi
Altri numeri di identificazione dello studio
- STUDY00009823B
- R37CA258376 (Sovvenzione/contratto NIH degli Stati Uniti)
Piano per i dati dei singoli partecipanti (IPD)
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Tipo di informazioni di supporto alla condivisione IPD
- STUDIO_PROTOCOLLO
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