When Patients Tell Their Own Stories: A Meta-Narrative Study of Web-Based Personalized Texts of 214 Melanoma Patients' Journeys in Four Countries

Malignant melanoma is an aggressive, recalcitrant disease. Its impact on people can be compounded by the physical and psychosocial consequences of medical management. Providing melanoma patients with patient-centered care that is effective, safe, and supportive throughout their journey requires knowledge of patients' progressive experiences and evolving perspectives. With ethical approval, we undertook a meta-narrative study of 214 experiential accounts of melanoma collected from the personal story sections of melanoma and cancer support websites. Using a narrative approach, we qualitatively examined the care experiences represented in these accounts and identified needs for supportive care in a framework reflective of the personal patient journey. We differentiate these across three key periods: lead-up to diagnosis; diagnosis, treatment, and recovery; and posttreatment and recurrence, and provide a visual representation of the patient journey. This article contributes to the growing body of work that utilizes Internet content as sources of qualitative, experiential health care data.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases

OBJECTIVE

The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases.

MATERIAL AND METHODS

Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas.

RESULTS

Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively.

CONCLUSION

The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols.