Letter in response to article – “Estimating the positive predictive value and sensitivity of the clinical diagnosis of basal cell carcinoma.”

2014 ◽  
Vol 67 (4) ◽  
pp. e122-e123
Author(s):  
Foiz Ahmed ◽  
Jonathan Bowling ◽  
Oliver Cassell
2021 ◽  
Author(s):  
Pushkar Aggarwal

BACKGROUND The performance of deep-learning image recognition models is below par when applied to images with Fitzpatrick classification skin types 4 and 5. OBJECTIVE The objective of this research was to assess whether image recognition models perform differently when differentiating between dermatological diseases in individuals with darker skin color (Fitzpatrick skin types 4 and 5) than when differentiating between the same dermatological diseases in Caucasians (Fitzpatrick skin types 1, 2, and 3) when both models are trained on the same number of images. METHODS Two image recognition models were trained, validated, and tested. The goal of each model was to differentiate between melanoma and basal cell carcinoma. Open-source images of melanoma and basal cell carcinoma were acquired from the Hellenic Dermatological Atlas, the Dermatology Atlas, the Interactive Dermatology Atlas, and DermNet NZ. RESULTS The image recognition models trained and validated on images with light skin color had higher sensitivity, specificity, positive predictive value, negative predictive value, and F1 score than the image recognition models trained and validated on images of skin of color for differentiation between melanoma and basal cell carcinoma. CONCLUSIONS A higher number of images of dermatological diseases in individuals with darker skin color than images of dermatological diseases in individuals with light skin color would need to be gathered for artificial intelligence models to perform equally well.


Iproceedings ◽  
10.2196/35441 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e35441
Author(s):  
Cristiane Benvenuto-Andrade ◽  
A Cognetta ◽  
D Manolakos

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.


2021 ◽  
pp. 1-4
Author(s):  
Airá Novello Vilar ◽  
Airá Novello Vilar ◽  
Clarissa Novello Batzner ◽  
João Avelleira ◽  
Arthur César Farah Ferreira

FNAC is commonly used in endocrinology, otorhinolaryngology and other areas, especially for evaluation of thyroid nodules, head and neck masses, enlarged lymph nodes and salivary gland abnormalities. Although FNAC is not a common practice in dermatology routine, in this prospective study, ninety-eight patients presenting with palpable lesions were submitted to FNAC and biopsy at the same time. The majority of cases (82 patients) were diagnosed as basal cell carcinoma on cytology, and had 100% of agreement with histopathology. Three cases presented as insufficient material in FNAC and all of them were diagnosed as superficial basal cell carcinoma in histopathology. All cases of squamous cell carcinoma (6 patients) were diagnosed accurately by FNAC. Two cases in our series were diagnosed as keratoacanthoma and due to the clinical correlation with cytopathology the report addressed this compatibility in a note; without the clinic it would be impossible to infer this diagnosis. All four cases of molluscum contagiosum showed characteristic cytopathological aspects and also had 100% of agreement with histopathology. The main potential use appears to be fastest results and confirmation of clinical diagnosis of basal cell carcinoma and squamous cell carcinoma to allow immediate referral for surgery. FNAC could also prove itself useful when the clinical diagnosis of molluscum contagiosum is among the clinical hypotheses, allowing to confirm it by viewing the characteristic intracytoplasmic inclusion bodies (molluscum bodies, or Henderson-Paterson bodies). The number of repeat out-patient clinic attendances could thus be reduced and valuable time saved on biopsy lists.


Skin Cancer ◽  
2006 ◽  
Vol 21 (3) ◽  
pp. 336-339
Author(s):  
Kazufumi YONEDA ◽  
Miho HAYASHI ◽  
Kana HIOKI ◽  
Kanako ASAI ◽  
Yuko HIRAMITSU ◽  
...  

2021 ◽  
pp. 12-12
Author(s):  
Milana Ivkov-Simic ◽  
Branislava Gajic ◽  
Dejan Ogorelica ◽  
Zorica Gajinov

Background/Aim. Growing incidence of skin tumors require their accurate diagnosis. Dermoscopy, especially in-vivo, enhances diagnosis of basal cell carcinoma (BCC). Total body skin examination (TBSE), a visual inspection of the patient?s total body surface, is considered basic step in dermatological exam, especially in skin cancer screening. However, TBSE is still a matter of debate of its expediency in real clinical setting. Aim of this study is to analyze diagnostic accuracy of BCC detected and treated by referred dermatologists in Skin Cancer Unit of a Dermatology and Venereology Clinic. Methods. Retrospective analysis of the BCC detection during total body skin examination with visual inspection and dermoscopy. We calculated sensitivity and specificity and positive predictive value for BCC using histopathological results as correct diagnosis. Results. Out of 3346 biopsied skin tumors 49.58% were malignant and 50.42% benign. The most common malignant tumor was BCC, accounting for 84.09%. Localization of BCCs was mainly on the trunk 38.92% and on H-zone of the face 37.63%. Other localizations were face (non-H-zone) 6.67%, neck 3.01%, scalp 3.37%, arms 6.88% and limbs 3.51%. Of all BCCs, 0.83% were recurrent BCC. The sensitivity for diagnosis BCC was 97.71%, and positive predictive value 95.08%. Conclusion. In dermatology setting, total body skin examination (TBSE) and visual inspection with in-vivo dermoscopy results with very good diagnostic performance of BCC.


2015 ◽  
Vol 95 (8) ◽  
pp. 996-998 ◽  
Author(s):  
M Roozeboom ◽  
H Kreukels ◽  
P Nelemans ◽  
K Mosterd ◽  
V Winnepenninckx ◽  
...  

2018 ◽  
Vol 30 (1) ◽  
pp. 64 ◽  
Author(s):  
Tea Hyung Ryu ◽  
Heesang Kye ◽  
Jae Eun Choi ◽  
Hyo Hyun Ahn ◽  
Young Chul Kye ◽  
...  

2018 ◽  
Vol 79 (1) ◽  
pp. 42-46 ◽  
Author(s):  
Hal Bret Willardson ◽  
Jamie Lombardo ◽  
Matt Raines ◽  
Tina Nguyen ◽  
Jisuk Park ◽  
...  

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