Phase I Amount
$1,291,948
Nonmelanoma skin cancer (NMSC) represents the most common form of cancer in the human body and causes twice as many fatalities each year as melanoma. The method for diagnosing and treating NMSCs requires a skin biopsy that is processed and stained for analysis on a standard optical microscope. This process is painful for patients, and the invasiveness of biopsy introduces a delay into NMSC detection, which contributes to patient morbidity and adds substantial cost to the healthcare system. Enspectra Health's mission is to bring digital oncology diagnostics to the point-of-care for earlier cancer detection where the healthcare cost and burden to patients is minimal. Cancer is an inherently cellular disfunction, and yet modern medicine still lacks the basic ability to view cellular histology without biopsy. This widespread clinical need is the core motivation for Enspectra and its innovations. Enspectra aims to address this unmet clinical need for a better method to detect NMSCs earlier. This direct to Phase II application builds on the progress of awarded Phase I, Phase II, and Phase IIB projects (R43CA221591, R44CA221591). In these projects, Enspectra has progressed from concept, through technical feasibility, and into clinical trials, recently completing enrollment in a pivotal trial for submission to the FDA for 510(k) approval (ClinicalTrials.gov: NCT05619471). Enspectra has created the first portable, fiber coupled, combined multiphoton microscopy (MPM) and reflectance confocal microscopy (RCM) system for in vivo imaging of NMSC. In this direct to Phase II proposal, Enspectra aims to leverage the analytic power of its multimodal data and extend its reach to Actinic Keratosis (AK), a precancerous lesion that can progress to NMSC. Enspectra will build a large-scale digital database of histopathology in AKs on patients before topical therapy. AKs that do not respond to therapy are more likely to progress to NMSC and are clinically of higher risk. Using the therapy outcome as an indicator of AK severity, Enspectra will train a deep learning algorithm to predict which AKs would be unresponsive solely on pathologic features in our noninvasive images. The ability to identify problematic AKs before they become malignant should improve surveillance of high-risk patients, hasten detection of NMSC, and lessen the burden of surgical intervention to low-risk patients.
Public Health Relevance Statement: Enspectra Health, Inc. is continuing its mission to increasingly move skin cancer detection earlier where the healthcare cost and burden to patients is minimal. In this project, we aim to pair the analytic power of the data generated by our novel, noninvasive cellular imaging technology (VIO) with deep learning algorithms to identify problematic precancers in patients before they progress to malignancy. By far, the greatest innovation of this project will be accomplishing our aims without the need of even a single biopsy.
Project Terms: Acceleration; Affect; Algorithms; Award; Base Sequence; Nucleotide Sequence; nucleic acid sequence; Biopsy; Malignant Neoplasms; Cancers; Malignant Tumor; malignancy; neoplasm/cancer; Squamous cell carcinoma; Epidermoid Carcinoma; Planocellular Carcinoma; Squamous Carcinoma; Squamous Cell Epithelioma; Classification; Systematics; Clinical Trials; Color; Dermatology; Diagnosis; Epidermis; Fluorouracil; 5-FU; 5-Fluracil; 5FU; Fluoro Uracil; Fluoruracil; Fluouracil; Goals; Health; Healthcare Systems; Health Care Systems; Histology; Human; Modern Man; Actinic keratosis; Actinic (Solar) Keratosis; Senile Hyperkeratosis; Solar Keratosis; senile keratosis; Lasers; Laser Electromagnetic; Laser Radiation; Learning; Literature; melanoma; Malignant Melanoma; Methods; Mission; Morbidity - disease rate; Morbidity; Motivation; Persons; Optics; optical; Pain; Painful; Pathology; Patients; Probability; Recurrence; Recurrent; Risk; Silicon; Si element; Computer software; Software; Stains; Staining method; Technology; Generations; Measures; Health Costs; Healthcare Costs; Health Care Costs; Data Set; Microscope; improved; early cancer detection; screening cancer patients; Screening for cancer; Clinical; Malignant; Malignant - descriptor; Phase; Histologically; Histologic; Multi-center trial; Multicenter Trials; Evaluation; Training; Lesion; Fiber; Modern Medicine; Data Bases; data base; Databases; Human Figure; Human body; Dermatologist; Personal Medical History; Personal Medical History Epidemiology; Medical History; Dysfunction; Physiopathology; pathophysiology; Functional disorder; Oncology Cancer; Oncology; skin cancer early detection; Screening for Skin Cancer; Diagnostic; Specimen; Research Specimen; Physiologic pulse; Pulse; Severities; Scanning; Slide; System; Operative Surgical Procedures; Operative Procedures; Surgical; Surgical Interventions; Surgical Procedure; surgery; American; photomultiplier; Histopathology; Biopsy Specimen; Biopsy Sample; novel; payment; Modality; Skin Carcinoma; Non-Melanoma Skin Cancer; nonmelanoma skin cancer; Sorting; Excision; Abscission; Extirpation; Removal; Surgical Removal; resection; Atypia; response; portability; Intervention; Intervention Strategies; interventional strategy; Skin; Thickness; Thick; Cream; Address; Data; Detection; Multimodal Imaging; multi-modal imaging; multi-modality imaging; multimodality imaging; premalignant; precancer; precancerous; Resolution; resolutions; in vivo; Cancer Detection; Enrollment; enroll; therapy outcome; therapeutic outcome; Patient-Focused Outcomes; Patient outcome; Patient-Centered Outcomes; patient oriented outcomes; Validation; validations; Pathologic; Preparation; preparations; Process; follow-up; Active Follow-up; active followup; follow up; followed up; followup; cellular imaging; cell imaging; point of care; Image; imaging; care burden; cost; digital; Outcome; Imaging technology; Coupled; innovate; innovative; innovation; high risk; imaging in vivo; in vivo imaging; noninvasive imaging; non-invasive imaging; spectral image; spectral imagery; spectrum image; spectrum imagery; spectrograph; imaging system; in vivo confocal microscopy; reflectance confocal microscopy; IVIS SpectrumCT; IVIS imaging; IVIS optical imaging; IVIS spectral imaging; IVIS spectrum; IVIS system; in vivo imaging system; Individualized Predictors; individualized predictions; personalized predictors; personalized predictions; digital pathology; multi-modal data; multi-modal datasets; multimodal datasets; multimodal data; deep learning algorithm; high dimensional data; multidimensional datasets; multidimensional data; multiphoton excitation microscopy; multiphoton microscopy; algorithm training; responsiveness prediction; predict responsiveness; progression risk
