The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.
OBJECTIVE
To investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.


METHODS
In total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis.


RESULTS
The estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners.


CONCLUSIONS
This study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions.
Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan Department of Dermatology, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan.
Wen-Yu
http://orcid.org/0000-0002-5376-9409
WY
Chang
Research Center for Adaptive Data Analysis, Center for Biomarkers and Translational Medicine, and Graduate Institute of Biomedical Engineering, National Central University, Jhongli, Taiwan.
Adam
A
Huang
Department of Dermatology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Yin-Chun
YC
Chen
Department of Family Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan Graduate Institute of Adult Education, National Kaohsiung Normal University, Kaohsiung, Taiwan.
Chi-Wei
CW
Lin
Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan Chiu Kun-Hsing Dermatology Clinic, Kaohsiung, Taiwan.
John
J
Tsai
Huang Yung-Hsueh Clinic, Changhua, Taiwan.
Chung-Kai
CK
Yang
Health Management Center, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
Yin-Tseng
YT
Huang
Department of Family Medicine, Taipei City Hospital, Renai Branch, Taipei, Taiwan Department of Psychology, National Chengchi University, Taipei, Taiwan.
Yi-Fan
YF
Wu
Department of Dermatology, Kaohsiung Medical University Hospital. Department of Dermatology, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Gwo-Shing
GS
Chen
D000328:Adult; D000368:Aged; D000465:Algorithms; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010781:Photography; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012871:Skin Diseases / Q000175:diagnosis*; D012878:Skin Neoplasms / Q000175:diagnosis; D012984:Software