Are We Missing Something? The Skin Lesions Not Seen in Teledermatology

Background The suspected skin cancer electronic referral pathway was introduced in 2017. It requires general practitioners to add regional, close-up, and dermoscopic images to a lesion-specific referral template for a teledermatologist to review and advise on management. The virtual lesion clinic is a nurse-led clinic used since 2010 to obtain high-quality images for teledermoscopy assessment. A limitation of both services is the absence of a full-body examination. Objective This study aims to evaluate the number of skin cancers missed during teledermatology assessment. Methods This is a retrospective review of skin lesion referrals to dermatology. Suspected skin cancer referrals made in the latter half of 2020 were compared with referrals to the virtual lesion clinic during a similar time period in 2016. Results The study included 481 patients with 548 lesions in the 2020 suspected skin cancer cohort that were matched for age, sex, and ethnicity to 400 patients with 682 lesions in the 2016 virtual lesion clinic cohort. A total of 41 patients underwent subsequent specialist review in the suspected skin cancer cohort compared to 91 in the virtual lesion clinic cohort. A total of 20% of the suspected skin cancer cohort and 24% of the virtual lesion clinic cohort were found to have at least one additional lesion of concern. The majority of these were keratinocytic skin cancers; there were 2 and 0 additional melanomas or melanoma-in-situ, respectively. The virtual lesion clinic nurses identified additional lesions for imaging in 78 of 400 (20%) patients assessed in the virtual lesion clinic. The teledermatologist determined (author AO) that 73% of these additional lesions were malignant. Of the 548 lesions, 10 (2%) in the suspected skin cancer group were rereferred, none of which had a change in diagnosis. Out of 682 lesions, 16 (2%) in the virtual lesion clinic cohort were rereferred, 6 (1%) of which had a change in diagnosis. Conclusions Patients diagnosed with skin cancer often have multiple lesions of concern. Single-lesion teledermoscopy diagnoses have high concordance with in-person evaluation and histology; however, we have shown that in-person examination may reveal other suspicious lesions. The importance of a full-body skin examination should be emphasized to the referrer. Acknowledgments The Waikato Medical Research Foundation provided financial support for the study. Conflicts of Interest None declared.

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Are We Missing Something? The Skin Lesions Not Seen in Teledermatology (Preprint)

10.2196/preprints.35393 ◽

2021 ◽

Author(s):

Leah Jones ◽

Amanda Oakley

Keyword(s):

Skin Cancer ◽

Skin Lesions ◽

Similar Time ◽

Additional Lesion ◽

Melanoma In Situ ◽

Cancer Group ◽

Skin Cancers ◽

Virtual Lesion ◽

Electronic Referral ◽

Full Body

BACKGROUND The suspected skin cancer electronic referral pathway was introduced in 2017. It requires general practitioners to add regional, close-up, and dermoscopic images to a lesion-specific referral template for a teledermatologist to review and advise on management. The virtual lesion clinic is a nurse-led clinic used since 2010 to obtain high-quality images for teledermoscopy assessment. A limitation of both services is the absence of a full-body examination. OBJECTIVE This study aims to evaluate the number of skin cancers missed during teledermatology assessment. METHODS This is a retrospective review of skin lesion referrals to dermatology. Suspected skin cancer referrals made in the latter half of 2020 were compared with referrals to the virtual lesion clinic during a similar time period in 2016. RESULTS The study included 481 patients with 548 lesions in the 2020 suspected skin cancer cohort that were matched for age, sex, and ethnicity to 400 patients with 682 lesions in the 2016 virtual lesion clinic cohort. A total of 41 patients underwent subsequent specialist review in the suspected skin cancer cohort compared to 91 in the virtual lesion clinic cohort. A total of 20% of the suspected skin cancer cohort and 24% of the virtual lesion clinic cohort were found to have at least one additional lesion of concern. The majority of these were keratinocytic skin cancers; there were 2 and 0 additional melanomas or melanoma-in-situ, respectively. The virtual lesion clinic nurses identified additional lesions for imaging in 78 of 400 (20%) patients assessed in the virtual lesion clinic. The teledermatologist determined (author AO) that 73% of these additional lesions were malignant. Of the 548 lesions, 10 (2%) in the suspected skin cancer group were rereferred, none of which had a change in diagnosis. Out of 682 lesions, 16 (2%) in the virtual lesion clinic cohort were rereferred, 6 (1%) of which had a change in diagnosis. CONCLUSIONS Patients diagnosed with skin cancer often have multiple lesions of concern. Single-lesion teledermoscopy diagnoses have high concordance with in-person evaluation and histology; however, we have shown that in-person examination may reveal other suspicious lesions. The importance of a full-body skin examination should be emphasized to the referrer.

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Dermatology

Oxford Handbook for Medical School ◽

10.1093/med/9780199681907.003.0010 ◽

2019 ◽

pp. 213-230

Keyword(s):

Skin Cancer ◽

Acne Vulgaris ◽

Systemic Disease ◽

Skin Lesions ◽

Punch Biopsy ◽

Stevens Johnson Syndrome ◽

Clinical Approach ◽

Dermatology Clinic ◽

Skin Cancers ◽

Johnson Syndrome

Dermatology is the study of the skin, hair, nails, and oral and genital mucus membranes and the diseases affecting them. It is predominantly an outpatient specialty. This chapter explains the common terminology used to describe skin lesions and dermatoses. The commonest conditions encountered in the dermatology clinic are described: eczema, psoriasis, pyoderma gangrenosum, skin cancers (basal cell skin cancer, squamous cell skin cancer, malignant melanoma), acne vulgaris and bullous disorders, in addition to dermatological manifestations of systemic disease such as vasculitis. Emergency presentations such as Stevens–Johnson syndrome/toxic epidermal necrolysis, anaphylaxis, and necrotizing fasciitis are outlined. A practical guide to common dermatological procedures such as punch biopsy, and a clinical approach to the dermatological patient are included.

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Recognising skin cancers in the dental patient

Faculty Dental Journal ◽

10.1308/204268512x13376834221631 ◽

2012 ◽

Vol 3 (3) ◽

pp. 158-165 ◽

Cited By ~ 2

Author(s):

Carrie Newlands

Keyword(s):

At Risk ◽

Skin Cancer ◽

Skin Lesions ◽

Facial Skin ◽

Older Population ◽

Skin Cancers ◽

Dental Patient ◽

The Face ◽

Fair Skin

Skin cancer is increasing in incidence and the face is the commonest site for skin cancers to occur. Patients who are at risk from skin cancers include those who have fair skin and who have had long-term exposure to sunshine.1 While facial skin cancers are more common in the older population, greater numbers of younger people are developing these cancers.2-4 Facial skin lesions are common. This article aims to help members of the dental team recognise the features of those lesions which may indicate malignancy or pre-malignancy.

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A 9-Year Teledermoscopy Service: Retrospective Service Review

Iproceedings ◽

10.2196/35401 ◽

2021 ◽

Vol 6 (1) ◽

pp. e35401

Author(s):

Novell Shu Chyng Teoh ◽

Amanda Oakley

Keyword(s):

Waiting Time ◽

Conflicts Of Interest ◽

Significant Risk ◽

Patient Characteristics ◽

Skin Lesions ◽

Number Needed To Treat ◽

Melanoma In Situ ◽

Male Sex

Background A teledermoscopy service was established in January 2010, where patients attended nurse-led clinics for imaging of lesions of concern and remote diagnosis by a dermatologist. Objective The study aimed to review the number of visits, patient characteristics, the efficiency of the service, and the diagnoses made. Methods We evaluated the waiting time and diagnosis of skin lesions for all patient visits from January 1, 2010, to May 31, 2019. The relationships between patient characteristics and the diagnosis of melanoma were specifically analyzed. Results The teledermoscopy clinic was attended by 6479 patients for 11,005 skin lesions on 8805 occasions. Statistically significant risk factors for the diagnosis of melanoma/melanoma in situ were male sex, European ethnicity, and Fitzpatrick skin type 2. Attendance was maximal during 2015 and 2016. The seasonal variation in visits 2011-2018 revealed a consistent peak at the end of summer and a dip at the end of winter. In the year 2010, 306 patients attended; 76% (233/306) of these were discharged to primary care and 24% (73/306) were referred to hospital for specialist assessment. For patients diagnosed by the dermatologist with suspected melanoma from January 1, 2010, to May 31, 2019, the median waiting time for an imaging appointment was 44.5 days (average 57.9 days, range 8-218 days). The most common lesions diagnosed were benign naevus (2933/11,005, 27%), benign keratosis (2576/11,005, 23%), and keratinocytic cancer (1707/11,005, 15%); melanoma was suspected in 5% (507/11,005) of referred lesions (Multimedia Appendix 1). The positive predictive value of melanoma/melanoma in situ was 61.1% (320 true positives and 203 false positives). The number needed to treat (ie, the ratio of the total number of excisions to the number with a histological diagnosis of melanoma/melanoma in situ) was 2.02. Conclusions Diagnoses were comparable to the experience of other teledermoscopy services. Teledermoscopy using a nurse-led imaging clinic can provide efficient and convenient access to dermatology by streamlining referrals to secondary care and prioritizing patients with skin cancer for treatment. Conflicts of Interest None declared.

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Accuracy of Store-and-Forward Teledermatology for the Diagnosis of Skin Cancer: The Nouvelle-Aquitaine Experience

Iproceedings ◽

10.2196/35404 ◽

2021 ◽

Vol 6 (1) ◽

pp. e35404

Author(s):

Colin Bui ◽

Marie-Sylvie Doutre ◽

Alain Taieb ◽

Marie Beylot-Barry ◽

Jean-Philippe Joseph ◽

...

Keyword(s):

Skin Cancer ◽

Conflicts Of Interest ◽

Precancerous Lesion ◽

Pathological Finding ◽

Skin Lesions ◽

University Hospital ◽

Median Response ◽

Day Range ◽

Intervention Area ◽

Store And Forward

Background In Nouvelle-Aquitaine (a French region with a population of almost 6 million), the density of dermatologists is less than 3.8/100,000 inhabitants. This lack of dermatological care is delaying diagnosis and management, especially for skin cancer. The SmartDerm Project is a store-and-forward (SAF) teledermatology platform for primary care in Nouvelle-Aquitaine providing dermatological counselling to general practitioners (GPs). Objective The main objective was to determine the concordance between the diagnosis of skin cancer made by dermatologists and the pathologists’ diagnosis. Methods GPs in 3 pilot departments of Nouvelle-Aquitaine (Lot-Et-Garonne, Deux-Sèvres, Creuse) sent their dermatology requests using their smartphone, via an app called PAACO/Globule; dermatologists at the University Hospital of Bordeaux answered within 48-72 hours. Consecutive cases of skin cancer suspected by the referent dermatologists during the intervention were included, if the result of biopsy interpreted by a certified pathologist was available at the time of the study. Results Among the 1727 requests, 163 (9%) concerned a possible diagnosis of skin cancer and were eligible. For 61 cases, the histopathological findings were not available. Eventually, 93 patients with a total of 102 skin lesions were included. Median age was 75 years (range 26-97 years), with 53% women. The skin lesions had progressed for 8 months on average (range 0.5-36 months). The median response time was 1 day (range 0-61 days); 65 days (range 1-667 days) elapsed on average between the SAF opinion and the histological sample. Histopathology diagnosed 83 malignant lesions (57 basal cell carcinomas, 69%; 18 squamous cell carcinomas, 22%; 6 melanomas, 7%; 1 cutaneous lymphoma, 1%; 1 secondary location of a primary cancer, 1%), 1 precancerous lesion, and 18 benign lesions. The concordance between the opinion of the referent dermatologist and the final pathological finding was 83% for nonmelanocytic lesions and 67% for melanocytic lesions. Conclusions This study showed the reliability of SAF teledermatology in the diagnosis of skin cancer, comparable to literature data in the absence of dermatoscopy. The median delay of about two months between request and histology was an improvement compared to the delay of usual appointments in the intervention area. The lack of data for 61 patients showed that SAF telemedicine requires better coordination and follow-up, especially for the management of skin cancer. With this reservation in mind, teledermatology offers an alternative answer for the triage of patients with skin cancer residing in areas with low medical density. Conflicts of Interest None declared.

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Assessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists

Iproceedings ◽

10.2196/35391 ◽

2021 ◽

Vol 6 (1) ◽

pp. e35391

Author(s):

Ibukun Oloruntoba ◽

Toan D Nguyen ◽

Zongyuan Ge ◽

Tine Vestergaard ◽

Victoria Mar

Keyword(s):

Skin Cancer ◽

Conflicts Of Interest ◽

Area Under The Curve ◽

Image Data ◽

Skin Lesions ◽

Training Image ◽

Data Sets ◽

Image Capture ◽

Data Set ◽

Unseen Data

Background Convolutional neural networks (CNNs) are a type of artificial intelligence that show promise as a diagnostic aid for skin cancer. However, the majority are trained using retrospective image data sets of varying quality and image capture standardization. Objective The aim of our study is to use CNN models with the same architecture, but different training image sets, and test variability in performance when classifying skin cancer images in different populations, acquired with different devices. Additionally, we wanted to assess the performance of the models against Danish teledermatologists when tested on images acquired from Denmark. Methods Three CNNs with the same architecture were trained. CNN-NS was trained on 25,331 nonstandardized images taken from the International Skin Imaging Collaboration using different image capture devices. CNN-S was trained on 235,268 standardized images, and CNN-S2 was trained on 25,331 standardized images (matched for number and classes of training images to CNN-NS). Both standardized data sets (CNN-S and CNN-S2) were provided by Molemap using the same image capture device. A total of 495 Danish patients with 569 images of skin lesions predominantly involving Fitzpatrick skin types II and III were used to test the performance of the models. Four teledermatologists independently diagnosed and assessed the images taken of the lesions. Primary outcome measures were sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUROC). Results A total of 569 images were taken from 495 patients (n=280, 57% women, n=215, 43% men; mean age 55, SD 17 years) for this study. On these images, CNN-S achieved an AUROC of 0.861 (95% CI 0.830-0.889; P<.001), and CNN-S2 achieved an AUROC of 0.831 (95% CI 0.798-0.861; P=.009), with both outperforming CNN-NS, which achieved an AUROC of 0.759 (95% CI 0.722-0.794; P<.001; P=.009). When the CNNs were matched to the mean sensitivity and specificity of the teledermatologists, the model’s resultant sensitivities and specificities were surpassed by the teledermatologists. However, when compared to CNN-S, the differences were not statistically significant (P=.10; P=.05). Performance across all CNN models and teledermatologists was influenced by the image quality. Conclusions CNNs trained on standardized images had improved performance and therefore greater generalizability in skin cancer classification when applied to an unseen data set. This is an important consideration for future algorithm development, regulation, and approval. Further, when tested on these unseen test images, the teledermatologists clinically outperformed all the CNN models; however, the difference was deemed to be statistically insignificant when compared to CNN-S. Conflicts of Interest VM received speakers fees from Merck, Eli Lily, Novartis and Bristol Myers Squibb. VM is the principal investigator for a clinical trial funded by the Victorian Department of Health and Human Services with 1:1 contribution from MoleMap.

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Incidence and Implications of Skin Cancers in Cancercare Manitoba Chronic Lymphocytic Leukemia (CLL) Clinic Patients

Blood ◽

10.1182/blood.v128.22.4359.4359 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4359-4359

Author(s):

Sara Beiggi ◽

Mohammad Pannu ◽

Versha Banerji ◽

Dhali H.S. Dhaliwal ◽

Spencer B. Gibson ◽

...

Keyword(s):

Skin Cancer ◽

Chronic Lymphocytic Leukemia ◽

Conflicts Of Interest ◽

Lymphocytic Leukemia ◽

P Value ◽

Close Monitoring ◽

Mutational Status ◽

Skin Cancers ◽

Increased Risk

Abstract Background:We previously conducted a population-based study on chronic lymphocytic leukemia (CLL) in Manitoba, which showed that second cancers are twice as common and skin cancers eight times as common in this disease, as compared to an age- and sex-matched control population and patients with follicular lymphoma. It is postulated that this is related to immunosuppression, secondary to the disease and chemo-immunotherapy. Here we set out to investigate rates and types of skin cancers in CLL patients and how these influence the outcome of CLL patients. Methods: Newly diagnosed CLL patients attending the CancerCare Manitoba CLL Clinic from the January 1st, 2002 until December 31st, 2012 were selected for this study. Patients were followed until December 31, 2014. Cox Proportional Hazard models were constructed to predict hazard's ratios (HR) and 95% confidence intervals (95% CI) for survival as well as risk of non-cutaneous malignancies. Association between skin cancer and CLL prognostic markers were investigated by Fisher's Exact test, Student's t-test and logistic regression analysis. P-value <0.05 was considered statistically significant. Statistical analysis was performed using SAS Studio 3.5. Results: There were 582 CLL patients in this study. The median age was 67 years (range 36-99 years) with a M:F ratio of 1.6:1. This compares with a median age of 71.5 years and a M:F ratio of 1.3:1 in the Manitoba CLL population. The median follow-up for the study was 5.8 years (range 0.1-13.0 years). There were 131 (23%) CLL patients with at least one skin cancer; 73 (56%) had their first skin cancer before the diagnosis of CLL and 58 (44%) after. Rates of first skin cancer diagnoses were constant before CLL diagnosis (5.2 per 1000 CLL cases), but began to increase three years prior to the CLL diagnosis (10.2 per 1000 CLL cases) and continued to increase after the CLL diagnosis (22.7 per 1000 CLL cases). There were a total of 368 skin cancers; 208 (57%) were basal cell carcinomas (BCC), 92 (25%) were squamous cell carcinomas (SCC), 47 (13%) were Bowen's disease, 18 (5%) were melanomas, and three (1%) were Merkel cell carcinomas. Interestingly, multiple skin cancers with varying histologies occurred in almost half the patients. When the total number of skin cancers/year was assessed, the number started to increase seven years before the CLL diagnosis and continued to increase yearly after the CLL diagnosis. Within the follow-up period, 154 (27%) patients died, with the major causes of death being CLL and second malignancies. However, the presence of skin cancers did not appear to influence survival. There were a total of three deaths due to skin cancers; two patients died of melanoma and one from BCC. However, the presence of a skin cancer, in CLL cases without a history of a solid tumor, increased the risk of a non-cutaneous malignancy by seven-fold (HR 7.55, 05% CI 3.92 - 14.53, p<0.0001). The presence of a skin cancer prior to the diagnosis of CLL did not predict CLL aggressiveness at diagnosis, as evaluated by Rai stage, Zap-70 or CD38 status, immunoglobulin levels or IGHV mutational status. However, for those patients developing their first skin cancer after the CLL diagnosis, the risk of developing a skin cancer correlated with the unmutated IGHV status (HR 1.54, 95% CI 1.01 - 2.34, p=0.0462) and baseline CD38 positivity (HR 1.58, 95% CI 1.02 - 2.44, p=0.0405). Interestingly, the risk of developing skin cancer was not increased by chemotherapy. Discussion: In summary, with a median follow-up of 5.8 years, 23% of patients had a skin cancer, half before the diagnosis of CLL and half after the CLL diagnosis. The incidence of skin cancers increased prior to the diagnosis of CLL, indicating that immunosuppression possibly preceded the diagnosis of CLL by years. The increased risk of developing skin cancers in patients with unmutated IGHV and CD38 positivity indicates that CLL patients with a more aggressive disease are more likely to develop skin cancer, probably due to a more pronounced immune deficiency. The diagnosis of skin cancer in CLL patients was associated with a seven-fold increased risk of developing a solid tumour. These results underscore the need for close monitoring and active surveillance of CLL patients for skin and other cancers throughout their disease course, by clinicians experienced in skin and other malignancies. Disclosures No relevant conflicts of interest to declare.

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ANN-based diagnosis method for skin cancers using dermoscopic images

Highlights in BioScience ◽

10.36462/h.biosci.202108 ◽

2021 ◽

pp. bs202108

Author(s):

Hamidreza Khezri ◽

Mojtaba Farzaneh ◽

Zeinab Ghasemishahrestani ◽

Ali Moghadam

Keyword(s):

Skin Cancer ◽

Accurate Result ◽

Low Cost ◽

Skin Lesions ◽

Multi Layer Perceptron ◽

Skin Cancers ◽

The World ◽

Diagnosis Method ◽

Artificial Neural Network Ann ◽

Melanoma Skin

Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

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Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology

Iproceedings ◽

10.2196/35395 ◽

2021 ◽

Vol 6 (1) ◽

pp. e35395

Author(s):

Harmony Thompson ◽

Amanda Oakley ◽

Michael B Jameson ◽

Adrian Bowling

Keyword(s):

Artificial Intelligence ◽

Primary Care ◽

Health Care ◽

Skin Cancer ◽

Sensitivity And Specificity ◽

Conflicts Of Interest ◽

Real Life ◽

Primary Care Providers ◽

Skin Lesions ◽

Care Providers

Background Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. Objective We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). Methods This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. Results Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). Conclusions The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care. Acknowledgments MoleMap NZ, who developed the AI algorithm, provided some funding for this study. HT's salary was partially sponsored by MoleMap NZ, who developed the AI algorithm. AB is a shareholder and consultant to Molemap Ltd provider of the AI algorithm. Conflicts of Interest None declared.

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Remote Skin Cancer Diagnosis: Adding Images to Electronic Referrals Is More Efficient Than Wait-Listing for a Nurse-Led Imaging Clinic

Cancers ◽

10.3390/cancers13225828 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5828

Author(s):

Leah Jones ◽

Michael Jameson ◽

Amanda Oakley

Keyword(s):

Primary Care ◽

Skin Cancer ◽

Time Course ◽

Skin Lesions ◽

Primary Objective ◽

Retrospective Comparison ◽

Virtual Lesion ◽

Primary Care Doctors ◽

Management Advice ◽

Diagnostic Concordance

We undertook a retrospective comparison of two teledermatology pathways that provide diagnostic and management advice for suspected skin cancers, to evaluate the time from referral to diagnosis and its concordance with histology. Primary Care doctors could refer patients to either the Virtual Lesion Clinic (VLC), a nurse-led community teledermoscopy clinic or, more recently, to the Suspected Skin Cancer (SSC) pathway, which requires them to attach regional, close-up, and dermoscopic images. The primary objective of this study was to determine the comparative time course between the SSC pathway and VLC. Secondary objectives included comparative diagnostic concordance, skin lesion classification, and evaluation of missed skin lesions during subsequent follow-up. VLC referrals from July to December 2016 and 2020 were compared to SSC referrals from July to December 2020. 408 patients with 682 lesions in the VLC cohort were compared with 480 patients with 548 lesions from the 2020 SSC cohort, matched for age, sex, and ethnicity, including histology where available. Median time (SD) from referral to receipt of teledermatology advice was four (2.8) days and 50 (43.0) days for the SSC and VLC cohorts, respectively (p < 0.001). Diagnostic concordance between teledermatologist and histopathologist for benign versus malignant lesions was 70% for 114 lesions in the SSC cohort, comparable to the VLC cohort (71% of 122 lesions). Referrals from primary care, where skin lesions were imaged with variable devices and quality resulted in faster specialist advice with similar diagnostic performance compared to high-quality imaging at nurse-led specialist dermoscopy clinics.

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