Sherea Stricklin

Melanoma in situ in a private practice setting 2005 through 2009: location, lesion size, lack of concern.

BACKGROUND Studies have shown that the incidence of melanoma in situ (MIS) is increasing significantly. OBJECTIVE This study analyzes selected clinical and demographic characteristics of MIS cases observed in private dermatology practices in the United States. METHODS This study collected 257 MIS cases from 4 private dermatology practices in the United States from January 2005 through December 2009, recording age, gender, anatomic location, lesion size, patient-reported change in lesion, and concern about lesion. Case totals for invasive melanoma during the same period were recorded. RESULTS The data collected showed a higher incidence of MIS in sun-exposed areas of older patients, especially men. The median age of patients at the time of MIS detection was 69 years. The most common site for MIS was the head-neck region. The number of MIS cases collected exceeded the number of invasive malignant melanoma cases during the study period, with an observed ratio of 1.35:1. LIMITATIONS For 136 patients, data were collected retrospectively for lesion size, location, gender, and age. For these patients, patient-reported change in lesion and concern about lesion were not collected. Patients often did not consent to a full body examination, therefore, it is possible that MIS lesions may have been missed in double-clothed areas. CONCLUSION Careful attention to pigmented lesions, even lesions less than 4 mm, on sun-exposed areas, including scalp, trunk, and feet, will facilitate earlier diagnosis of MIS. As only 30.4% of male patients and 50% of female patients had concern about these lesions, it still falls to the dermatologist to discover MIS.

Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification.

Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the USA. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue‐gray ovoids, leaf‐structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features.

Automatic dirt trail analysis in dermoscopy images.

Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails.

DISCRIMINATION OF BASAL CELL CARCINOMA FROM BENIGN LESIONS BASED ON EXTRACTION OF ULCER FEATURES IN POLARIZED-LIGHT DERMOSCOPY IMAGES

Ulcers are frequently visible in magnified, cross‐polarized, dermoscopy images of basal cell carcinoma. An ulcer without a history of trauma, a so‐called ‘atraumatic’ ulcer, is an important sign of basal cell carcinoma, the most common skin cancer. Distinguishing such ulcers from similar features found in benign lesions is challenging. In this research, color and texture features of ulcers are analyzed to discriminate basal cell carcinoma from benign lesions.

Sector expansion and elliptical modeling of blue-gray ovoids for basal cell carcinoma discrimination in dermoscopy images

Blue‐gray ovoids (B‐GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B‐GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B‐GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B‐GOs from their benign mimics.

Region growing by sector analysis for detection of blue‐gray ovoids in basal cell carcinoma

Blue‐gray ovoids (B‐GOs) are critical dermoscopic structures in basal cell carcinomas (BCCs) that pose a challenge for automatic detection. Due to variation in size and color, B‐GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could help further accomplish the goal of automatic BCC detection. This study introduces an efficient sector‐based method for segmenting B‐GOs. Four modifications of conventional region‐growing techniques are presented: (i) employing a seed area rather than a seed point, (ii) utilizing fixed control limits determined from the seed area to eliminate re‐calculations of previously‐added regions, (iii) determining region growing criteria using logistic regression, and (iv) area analysis and expansion by sectors. Contact dermoscopy images of 68 confirmed BCCs having B‐GOs were obtained. A total of 24 color features were analyzed for all B‐GO seed areas. Logistic regression analysis determined blue chromaticity, followed by red variance, were the best features for discriminating B‐GO edges from surrounding areas. Segmentation of malignant structures obtained an average Pratts figure of merit of 0.397. The techniques presented here provide a non‐recursive, sector‐based, region‐growing method applicable to any colored structure appearing in digital images. Further research using these techniques could lead to automatic detection of B‐GOs in BCCs.