Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Diseases and Abnormalities at or Before Birth

Breast Neoplasms

Congenital Abnormalities

Skin Abnormalities

Breast Diseases

Skin Diseases

adipose SVF cell transfer to the half of irradiated breast

Normal saline inject to the half of irradiated breast

Adipose SVF cell

Normal saline

Chanyeong Heo, Ph.D

The aim of this study is to evaluate the skin quality of using adipose-drived stromal vascular fraction (SVF) cell taken by automatic centrifuge for adipose-drived cell isolation system into irradiated breasts.

10 participants who taken conservative mastectomy will be enrolled

1. Process

   * adipose tissue is obtained from the subject's thigh or abdomen
   * adipose-drived stromal vascular fraction(SVF)cell isolation using automatic centrifuge system
   * adipose-drived stromal vascular fraction(SVF)cell graft into the irradiated breast.
2. Duration of study

   * entire duration: approximate 30weeks
   * Follow-up period: 12 weeks
   * Enrollment period: 12 weeks
3. Study design - Randomized, prospective, pilot study

Pilot Study of Skin Quality Improvement After Adipose-drived Stem Cell Transfer in Irradiated Breasts

The efficacy is assessed by breast skin thickness measured by radiologist using breast ultrasonography

Seoul National University Hospital

Skin Neoplasms

To investigate the potential for the VIO System to display microscopic skin structure in people of different age, sex, race, and skin health

VIO System

The overall objective of this study is to investigate the potential for the VIO System to display microscopic skin structure in people of different age, sex, race, and skin health.

Non-Linear Imaging of Skin In Vivo

Non-Linear Imaging of Skin In Vivo: VIO In Vivo Skin Imaging evaluatiON (VISION)

Quantify imaging performance in various types of skin, which may include estimated in vivo resolution and imaging depth. Various types of skin includes skin of different people as well as skin in different locations on the body, and skin with skin disease, including basal cell carcinoma.

VIO System device design characteristics were iterated during the study to optimize image quality across subjects with various skin types, body locations, and skin disease states. The outcome measure of device imaging performance (acceptable/not acceptable) was assessed through engineering expert review of VIO images. The engineering expert review determined whether images were of consistent quality.

Qualitatively evaluate image quality and interpretability by physicians trained in dermatopathology as a function of skin type.

VIO System device design characteristics were iterated during the study to optimize image quality across subjects with various skin types, body locations, and skin disease states. The outcome measure of device image quality (acceptable/not acceptable) was assessed through clinical expert review of VIO images. The clinical expert review determined whether VIO images could be interpreted by physicians trained in dermatopathology.

National Institutes of Health (NIH)

National Cancer Institute (NCI)

Enspectra Health

Healthy subjects, volunteers as well subjects with suspected skin condition recruited from dermatology clinics

VIO Imaging

Age, Continuous

Sex: Female, Male

Ethnicity (NIH/OMB)

Race (NIH/OMB)

United States

Region of Enrollment

Fitzpatrick skin type is a demographic measure of skin pigment and sensitivity to sunlight, as self-reported by participants prior to the study procedure. The measure ranges from Type I (least pigment/most sensitive) to Type VI (most pigment/least sensitive). Refer to PubMed ID: 29461771 for the scale used.

Fitzpatrick Skin Type

Healthy

Suspected or known skin diseases

Skin Health

President

VIO System Imaging Performance assessment was conducted on images taken of all participants

VIO System Imaging Performance

VIO System Image Quality assessment was conducted on images taken of all participants

VIO System Image Quality

Overall Study

Subjects with skin conditions that are candidates for biopsy

Skin conditions that are candidates for skin biopsies

Michael Wang, MD, FAAD

To demonstrate the safety and effectiveness of the VIO device in obtaining in vivo images that show tissue features including epidermis, dermis, collagen, blood vessels, and/or pigment.

To demonstrate that the tissue features identified on the images obtained with the VIO device align with the corresponding pathology images procured from the skin biopsy.

To evaluate the ability of blinded readers to correctly identify tissue features on images obtained with the VIO device.

VIO Imaging for Skin Tissue Assessment (VISTA)

VIO Imaging for Skin Tissue Assessment (VISTA) - a Prospective, Multicenter Investigation of the VIO Device in Subjects Undergoing Routine Biopsy

1. 100% agreement and validation of specific tissue features on VIO images (epidermis, dermis, collagen, blood vessels and pigment) in comparison to gold standard pathology images in Comparative Reader assessment.

2. \>90% agreement between Blinded Reader VIO image assessment and answer key developed from validation assessment.

3. Safety will be assessed as the incidence of all adverse events (analyzed by severity, seriousness, and relationship to the device and procedure) that occur through the 7-day follow-up period.

1) 90% inter-reader agreement during image analysis

Evaluation of secondary histopathology characteristics using a questionnaire to identify skin features on the images captured on the investigational device VIO; including but not limited to

* Dermal elastosis
* Cellular nodules within dermis will be evaluated during image analysis
* Large keratinocytes

Musculoskeletal Diseases

Rare Diseases

Carcinoma

Carcinoma, Squamous Cell

Genetic Diseases, Inborn

Epidermolysis Bullosa

Epidermolysis Bullosa Dystrophica

Skin Diseases, Vesiculobullous

Skin Diseases, Genetic

Rheumatic Diseases

Collagen Diseases

Connective Tissue Diseases

Dystrophic Epidermolysis Bullosa

Recessive Dystrophic Epidermolysis Bullosa

Participants will complete an online survey and submit photographs.

online survey

In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early.

SCCs which rapidly metastasize are the main cause of death in adults with RDEB. The earlier an SCC is recognized, the easier it can be removed and the better the outcome. AI leverages computer science to perform tasks that typically require human intelligence and has recently been used to identify skin cancers based on images. We are currently developing an AI approach for early detection of SCC and distinction of malignancy from chronic wounds and other RDEB skin findings. The aim is to create a web application for patients with RDEB to upload images of their skin and get an output as to SCC present/ no SCC. This will be especially valuable for patients with difficult access to medical expertise and those who are hesitant to allow full skin examination at each visit, often because of fear of biopsies. Thus, this project will directly benefit patients by allowing early recognition of SCCs and will empower patients and their families by providing a home use tool.

So far, the study team has mainly used professional images (photographs taken in hospital settings by physicians, nurses, and clinical photographers) of both SCCs in RDEB and images of RDEB skin without SCC to develop and train the AI model. The images that are expected in a real-life setting will mostly be pictures taken by patients or family members with their phones or digital cameras. These images have different properties regarding resolution, focus, lighting, and backgrounds. Incorporating such images will be crucial in the upcoming phases of model development-testing and validation-for the web application be a success for patients.

This project will enroll adolescents and adults with RDEB and history of at least one SCC. The survey and consents will be provided in English, Spanish, German, French, Arabic, Chinese, and Russian. The study team is inviting people with RDEB around the world to participate and are hoping that approximately 100 people will provide images.

Participants will be asked to complete the survey and upload photographs of SCC(s) using the links below. Depending on the number of SCCs they have had and the number of photos they want to provide, the survey will take approximately 15-20 minutes to complete.

To participate in this study, please follow this link:

https://redcap.nubic.northwestern.edu/redcap/surveys/?s=JH9LHR4CC4R4H3HN

Artificial Intelligence Patient App for RDEB SCCs

Developing a Novel Artificial Intelligence Patient App to Recognize Squamous Cell Carcinoma (SCCs) in Recessive Dystrophic Epidermolysis Bullosa (RDEB): Image Collection

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Pilot Study of Skin Quality Improvement After Adipose-drived Stem Cell Transfer in Irradiated Breasts	NCT01801878	Breast Neoplasm... Skin Abnormalit...	Adipose SVF cel... Normal saline	40 Years - 60 Years	Seoul National University Hospital
Non-Linear Imaging of Skin In Vivo	NCT05410964	Skin Cancer Skin Condition Skin Diseases Skin Lesion Skin Abnormalit...	VIO System	2 Years - 90 Years	Enspectra Health
Non-Linear Imaging of Skin In Vivo	NCT05410964	Skin Cancer Skin Condition Skin Diseases Skin Lesion Skin Abnormalit...	VIO System	2 Years - 90 Years	Enspectra Health
VIO Imaging for Skin Tissue Assessment (VISTA)	NCT05619471	Skin Condition Skin Diseases Skin Lesion Skin Abnormalit... Skin Cancer	VIO Imaging	18 Years - 99 Years	Enspectra Health
Artificial Intelligence Patient App for RDEB SCCs	NCT05843994	Epidermolysis B...	online survey	12 Years -	Northwestern University

Spots Global Cancer Trial Database for skin abnormalities