The Clinical Utility of a Handheld Elastic Scattering Spectroscopy Tool and Machine Learning in the Diagnosis and Referral Management of Skin Cancer by Primary Care Physicians

Background Elastic scattering spectroscopy (ESS) is a noninvasive optical biopsy technique that can distinguish between normal and abnormal tissue in vivo. The handheld device measures ESS spectra of skin lesions and classifies lesions with an output of “Investigate Further” or “Monitor.” The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. The device performance was also evaluated in an associated clinical study. Objective The aim of this paper was to establish whether the use of a handheld ESS tool can improve the detection of skin malignancies by evaluating clinical performance while emulating a real-world telemedicine clinical care setting. Methods The associated clinical study examined an independent test set of 332 lesions in a prospective multicenter study that compared algorithm performance to biopsy results for diagnosing malignant lesions. A total of 50 cases were randomly selected from the study data base (25 malignant and 25 benign lesions). Device performance on these lesions had a 96% sensitivity. High-resolution digital images and the patient’s clinical information including prior skin cancer history, risk factors, and physical examination results were available for evaluation. A total of 57 primary care physicians participated in this study in 2 phases, the first phase with their standard-of-care diagnostic and the second phase regarding their evaluation with the device output. The physicians were educated on the ESS device before evaluating the cases in a random order. Case evaluation included the physician reporting their diagnosis, management decision, and confidence level without the device output in the first phase and with the device output in the second phase. The results were evaluated for sensitivity and specificity with confidence intervals. Results The diagnostic sensitivity of the readers without and with the use of the handheld ESS device increased significantly from 67% to 88% (P<.001). There was no significant difference in specificity at 40% and 53% (P=.05). The management sensitivity of the readers increased significantly with and without the use of the device, which, respectively, was 94% (91%-96%) and 81% (77%-85%) (P<.001), suggesting that the use of the device may reduce false negatives by 68%. Specificity was comparable for management decisions (P=.36) at 31% compared to 36% without the device. Conclusions The use of the handheld ESS device significantly improved diagnostic and management sensitivity over standard-of-care, with comparable specificity. While telemedicine has shown promise in many fields, studies have shown that in-person skin evaluation is superior to telemedicine evaluations; however, integration with this type of tool has the potential to improve early detection.

The Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions for Cancer

Background Elastic scattering spectroscopy (ESS) is an optical biopsy technique that can distinguish between a normal and abnormal tissue in vivo without the need to remove it. The handheld device measures ESS spectra of skin lesions and classifies lesions as either malignant or benign with an output of “Investigate Further” or “Monitor,” respectively, with positive results accompanied by a spectral score output from 1 to 10, indicating how similar the lesion is to the malignant lesions the device was trained on. The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. Objective The purpose of this study was to evaluate the safety and effectiveness of the handheld ESS device in detecting the most common types of skin cancer. Methods A prospective, single-arm, investigator-blinded, multicenter study conducted at 4 investigational sites in the United States was performed. Patients who presented with skin lesions suggestive of melanoma, basal cell carcinoma, squamous cell carcinoma, and other highly atypical lesions were evaluated with the handheld ESS device. A validation performance analysis was performed with 553 lesions from 350 subjects with algorithm version 2.0. An independent test set of 281 lesions was selected and used to evaluate device performance in the detection of melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Statistical analyses included overall effectiveness analyses for sensitivity and specificity as well as subgroup analyses for lesion diagnoses. Results The overall sensitivity of the device was 92.3% (95% CI: 87.1 to 95.5%). The sensitivity for subgroups of lesions was 95% (95% CI 75.1% to 99.9%) for melanomas, 94.4% (95% CI 86.3% to 98.4%) for BCCs, and 92.5% (95% CI 83.4% to 97.5%) for SCCs. The overall device specificity was 36.6% (95% CI 29.3% to 44.6%). There was no statistically significant difference between the dermatologist performance and the ESS device (P=.2520). The specificity of the device was highest for benign melanocytic nevi (62.5%) and seborrheic keratoses (78.2%). The overall positive predictive value (PPV) was 59.8%, and the negative predictive value (NPV) was 81.9% with the study’s malignancy prevalence rate of 51%. For a prevalence rate of 5%, the PPV was estimated to be 7.1%, and the NPV was estimated to be 98.9%. For a prevalence rate of 7%, the PPV was estimated to be 9.8%, and the NPV was estimated to be 98.4%. For a prevalence rate of 15%, the PPV was estimated to be 20.3%, and the NPV was 96.4%. Conclusions The handheld ESS device has a high sensitivity for the detection of melanoma, BCC, and SCC. Coupled with clinical exam findings, this device can aid physicians in detecting a variety of skin malignancies. The device output can aid teledermatology evaluations by helping frontline providers determine which lesions to share for teledermatologist evaluation as well as potentially benefitting teledermatologists’ virtual evaluation, especially in instances of suboptimal photo quality. Acknowledgments This study was sponsored by Dermasensor Inc. Conflicts of Interest None declared.

The Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions for Cancer (Preprint)

BACKGROUND Elastic scattering spectroscopy (ESS) is an optical biopsy technique that can distinguish between a normal and abnormal tissue in vivo without the need to remove it. The handheld device measures ESS spectra of skin lesions and classifies lesions as either malignant or benign with an output of “Investigate Further” or “Monitor,” respectively, with positive results accompanied by a spectral score output from 1 to 10, indicating how similar the lesion is to the malignant lesions the device was trained on. The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. OBJECTIVE The purpose of this study was to evaluate the safety and effectiveness of the handheld ESS device in detecting the most common types of skin cancer. METHODS A prospective, single-arm, investigator-blinded, multicenter study conducted at 4 investigational sites in the United States was performed. Patients who presented with skin lesions suggestive of melanoma, basal cell carcinoma, squamous cell carcinoma, and other highly atypical lesions were evaluated with the handheld ESS device. A validation performance analysis was performed with 553 lesions from 350 subjects with algorithm version 2.0. An independent test set of 281 lesions was selected and used to evaluate device performance in the detection of melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Statistical analyses included overall effectiveness analyses for sensitivity and specificity as well as subgroup analyses for lesion diagnoses. RESULTS The overall sensitivity of the device was 92.3% (95% CI: 87.1 to 95.5%). The sensitivity for subgroups of lesions was 95% (95% CI 75.1% to 99.9%) for melanomas, 94.4% (95% CI 86.3% to 98.4%) for BCCs, and 92.5% (95% CI 83.4% to 97.5%) for SCCs. The overall device specificity was 36.6% (95% CI 29.3% to 44.6%). There was no statistically significant difference between the dermatologist performance and the ESS device (<i>P</i>=.2520). The specificity of the device was highest for benign melanocytic nevi (62.5%) and seborrheic keratoses (78.2%). The overall positive predictive value (PPV) was 59.8%, and the negative predictive value (NPV) was 81.9% with the study’s malignancy prevalence rate of 51%. For a prevalence rate of 5%, the PPV was estimated to be 7.1%, and the NPV was estimated to be 98.9%. For a prevalence rate of 7%, the PPV was estimated to be 9.8%, and the NPV was estimated to be 98.4%. For a prevalence rate of 15%, the PPV was estimated to be 20.3%, and the NPV was 96.4%. CONCLUSIONS The handheld ESS device has a high sensitivity for the detection of melanoma, BCC, and SCC. Coupled with clinical exam findings, this device can aid physicians in detecting a variety of skin malignancies. The device output can aid teledermatology evaluations by helping frontline providers determine which lesions to share for teledermatologist evaluation as well as potentially benefitting teledermatologists’ virtual evaluation, especially in instances of suboptimal photo quality.

The Clinical Utility of a Handheld Elastic Scattering Spectroscopy Tool and Machine Learning in the Diagnosis and Referral Management of Skin Cancer by Primary Care Physicians (Preprint)

BACKGROUND Elastic scattering spectroscopy (ESS) is a noninvasive optical biopsy technique that can distinguish between normal and abnormal tissue in vivo. The handheld device measures ESS spectra of skin lesions and classifies lesions with an output of “Investigate Further” or “Monitor.” The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. The device performance was also evaluated in an associated clinical study. OBJECTIVE The aim of this paper was to establish whether the use of a handheld ESS tool can improve the detection of skin malignancies by evaluating clinical performance while emulating a real-world telemedicine clinical care setting. METHODS The associated clinical study examined an independent test set of 332 lesions in a prospective multicenter study that compared algorithm performance to biopsy results for diagnosing malignant lesions. A total of 50 cases were randomly selected from the study data base (25 malignant and 25 benign lesions). Device performance on these lesions had a 96% sensitivity. High-resolution digital images and the patient’s clinical information including prior skin cancer history, risk factors, and physical examination results were available for evaluation. A total of 57 primary care physicians participated in this study in 2 phases, the first phase with their standard-of-care diagnostic and the second phase regarding their evaluation with the device output. The physicians were educated on the ESS device before evaluating the cases in a random order. Case evaluation included the physician reporting their diagnosis, management decision, and confidence level without the device output in the first phase and with the device output in the second phase. The results were evaluated for sensitivity and specificity with confidence intervals. RESULTS The diagnostic sensitivity of the readers without and with the use of the handheld ESS device increased significantly from 67% to 88% (<i>P</i>&lt;.001). There was no significant difference in specificity at 40% and 53% (<i>P</i>=.05). The management sensitivity of the readers increased significantly with and without the use of the device, which, respectively, was 94% (91%-96%) and 81% (77%-85%) (<i>P</i>&lt;.001), suggesting that the use of the device may reduce false negatives by 68%. Specificity was comparable for management decisions (<i>P</i>=.36) at 31% compared to 36% without the device. CONCLUSIONS The use of the handheld ESS device significantly improved diagnostic and management sensitivity over standard-of-care, with comparable specificity. While telemedicine has shown promise in many fields, studies have shown that in-person skin evaluation is superior to telemedicine evaluations; however, integration with this type of tool has the potential to improve early detection.