Interobserver variability on the histopathologic diagnosis of cutaneous melanoma and other pigmented skin lesions.

1996 ◽  
Vol 14 (4) ◽  
pp. 1218-1223 ◽  
Author(s):  
R Corona ◽  
A Mele ◽  
M Amini ◽  
G De Rosa ◽  
G Coppola ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Kappa Statistic ◽  
Lymphocytic Infiltration ◽  
Skin Lesions ◽  
Small Subset ◽  
Pigmented Lesions ◽  
Kappa Value ◽  
Diagnosis And Classification ◽  
Pigmented Skin Lesions

PURPOSE To assess the interobserver agreement on the diagnosis and classification of cutaneous melanoma. MATERIALS AND METHODS A set of 140 slides of cutaneous melanoma, including a small subset of benign pigmented skin lesions, were circulated to four experienced histopathologists. The kappa statistic for multiple ratings per subject was calculated using the method described by Fleiss. RESULTS The kappa value on the diagnosis of cutaneous melanoma versus benign lesions was 0.61. There was some discordance on the diagnosis in 37 of 140 cases (26%). For the histopathologic classification of cutaneous melanoma, the highest kappa values were attained for Breslow thickness (kappa = 0.76) and presence of ulceration (kappa = 0.87). The agreement was generally poor for other histologic features, such as level of dermal invasion (kappa = 0.38), presence of regression (kappa = 0.27), and lymphocytic infiltration (kappa = 0.27). CONCLUSION Our study suggests considerable disagreement among pathologists on the diagnosis of melanoma versus other pigmented lesions. Tumor thickness and presence of ulceration are the most reproducible histologic features of cutaneous melanoma.

Teledermoscopy - results of a multicentre study on 43 pigmented skin lesions

2000 ◽  
Vol 6 (3) ◽  
pp. 132-137 ◽  
Author(s):  
Domenico Piccolo ◽  
Josef Smolle ◽  
Giuseppe Argenziano ◽  
Ingrid H Wolf ◽  
Ralph Braun ◽  
...  
Keyword(s):  
Multicentre Study ◽  
Gold Standard ◽  
Skin Lesions ◽  
Reliable Technique ◽  
Face To Face ◽  
Pigmented Lesions ◽  
Basal Cell Carcinomas ◽  
Kappa Value ◽  
Melanocytic Naevi ◽  
Pigmented Skin Lesions

We performed a multicentre study to evaluate the agreement between the direct clinical diagnosis and the telediagnosis of 43 cutaneous pigmented lesions. Digital clinical and dermoscopic images of the 43 pigmented skin lesions (11 melanomas, 23 melanocytic naevi, three basal cell carcinomas, three lentigines, two seborrhoeic keratoses and one angiokeratoma) were sent by email to 11 colleagues (six dermatologists, two residents in dermatology, one oncologist, one specialist in internal medicine and one general practitioner) in 10 centres. These 11 colleagues had different degrees of experience in dermoscopy. With histopathology as the gold standard, an average of 85% of the telediagnoses were correct, with results varying from 77% to 95%, whereas face-to-face diagnosis by an expert dermatologist was correct in 91% of cases. The kappa value for all participants ranged from 0.35 to 0.87. The results confirm that teledermoscopy can be a reliable technique for the diagnosis of pigmented skin lesions but one that will depend on the expertise of the observer.

Working Formulation (WF) and Revised European-American classification of Lymphoma (REAL): Inter-observer agreement in conflicting lymphoma cases

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 17567-17567
Author(s):  
S. Latifzadeh ◽  
T. Riahi ◽  
V. Entezari
Keyword(s):  
Kappa Statistic ◽  
Clinical Findings ◽  
Observer Agreement ◽  
Aggressive Lymphoma ◽  
Low Grade ◽  
High Grade ◽  
Diagnosis And Classification ◽  
Clinical Risks ◽  
High Level

17567 Background: Immunophenotypic and genetic studies play an increasingly important role in diagnosis and classification of lymphoid neoplasm. This study tried to re-evaluate a number of conflicting lymphoma cases which were reported by WF previously, with REAL classification and to measure the agreement between these two methods of classification. Methods: In a three year period, a panel of expert pathologists evaluated referral cases by WF. Those cases (n = 60, Mean age = 40.9 ± 16.4) whose evaluations did not reached to a definitive pathologic diagnosis or there was a discrepancy between their pathologic and clinical findings were reviewed in Keil institute of hematopathology in Germany based on REAL classification. The primary and secondary diagnoses each were classified in five subgroups with equivalent clinical risks (see Table ). Results: Disagreement was detected in 23 cases (38%), while exact kappa statistic was 0.50. Sixteen cases (70%) of difference belonged to group of low grade lymphoma (kappa = 0.35) in which 11 cases (69%) changed to aggressive lymphoma and one case changed to highly aggressive subgroups. Four cases (25%) of difference occurred in the group of low probability lymphoma in which neoplasia was documented. High grade and Hodgkin lymphoma subgroups showed a high level of agreement (kappa = 0.84 and 0.74 respectively). Conclusions: Based on this study’s results, it can be concluded that there is a moderate agreement between WF and REAL classifications in conflicting lymphoma cases. WF underestimates clinical risk of low grade lymphoma in a considerable amount of patients but in high grade lymphoma the disagreement is not so high. [Table: see text] No significant financial relationships to disclose.

Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

2021 ◽  
Author(s):  
Sivaraj S ◽  
Dr.R. Malmathanraj
Keyword(s):  
Skin Lesion ◽  
Region Growing ◽  
Skin Lesions ◽  
Classification Approach ◽  
Probability Map ◽  
Knn Classifier ◽  
Knn Classification ◽  
Pigmented Skin Lesions ◽  
Thresholding Algorithm

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

scholarly journals Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

2015 ◽  
Vol 24 (6) ◽  
pp. 061104 ◽  
Author(s):  
Víctor González-Castro ◽  
Johan Debayle ◽  
Yanal Wazaefi ◽  
Mehdi Rahim ◽  
Caroline Gaudy-Marqueste ◽  
...  
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions

Assessment of cutaneous melanoma and pigmented skin lesions with photoacoustic imaging

2015 ◽  
Author(s):  
Aedán Breathnach ◽  
Liz Concannon ◽  
Laura Aalto ◽  
Jemima Dorairaj ◽  
Hrehesh M. Subhash ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Photoacoustic Imaging ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

scholarly journals A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

2011 ◽  
Vol 23 (2) ◽  
pp. 121 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Color Image ◽  
Skin Lesions ◽  
Benign Lesions ◽  
Data Set ◽  
Pigmented Lesions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

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