Confocal microscopy, dermoscopy, and histopathology features of atypical intraepidermal melanocytic proliferations associated with evolution to melanoma in situ

In vivo reflectance confocal microscopy can detect the invasive component of lentigo maligna melanoma: Prospective analysis and case-control study

BACKGROUND

Lentigo maligna (LM), a form of melanoma in situ, has no risk of causing metastasis unless dermal invasive melanoma (LMM) supervenes. Furthermore, the detection of invasion impacts prognosis and management.

OBJECTIVE

To assess the accuracy of RCM for the detection of invasion component on LM/LMM lesions.

METHODS

In the initial case-control study, the performance of one expert in detecting LMM at the time of initial RCM assessment of LM/LMM lesions was recorded prospectively (n = 229). The cases were assessed on RCM-histopathology correlation sessions and a panel with nine RCM features was proposed to identify LMM, which was subsequently tested in a subset of initial cohort (n = 93) in the matched case-control study by two blinded observers. Univariable and multivariable logistic regression models were performed to evaluate RCM features predictive of LMM. Reproducibility of assessment of the nine RCM features was also evaluated.

RESULTS

A total of 229 LM/LMM cases evaluated by histopathology were assessed blindly and prospectively by an expert confocalist. On histopathology, 210 were LM and 19 were LMM cases. Correct identification of an invasive component was achieved for 17 of 19 LMM cases (89%) and the absence of a dermal component was correctly diagnosed in 190 of 210 LM cases (90%). In the matched case-control (LMM n = 35, LM n = 58), epidermal and junctional disarray, large size of melanocytes and nests of melanocytes were independent predictors of LMM on multivariate analysis. The interobserver analysis demonstrated that these three features had a fair reproducibility between the two investigators (K = 0.4). The multivariable model including those three features showed a high predictive performance AUC = 74% (CI 95% 64-85%), with sensitivity of 63% (95% CI 52-78%) and specificity of 79% (CI 95% 74-88%), and likelihood ratio of 18 (p-value 0.0026).

CONCLUSION

Three RCM features were predictive for identifying invasive melanoma in the background of LM.

Computer-Aided Diagnosis of Melanoma Subtypes Using Reflectance Confocal Images

Lentigo maligna (LM) is an early form of pre-invasive melanoma that predominantly affects sun-exposed areas such as the face. LM is highly treatable when identified early but has an ill-defined clinical border and a high rate of recurrence. Atypical intraepidermal melanocytic proliferation (AIMP), also known as atypical melanocytic hyperplasia (AMH), is a histological description that indicates melanocytic proliferation with uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may, in some cases, progress to LM. The early diagnosis and distinction of LM from AIMP are important since LM requires a definitive treatment. Reflectance confocal microscopy (RCM) is an imaging technique often used to investigate these lesions non-invasively, without biopsy. However, RCM equipment is often not readily available, nor is the associated expertise for RCM image interpretation easy to find. Here, we implemented a machine learning classifier using popular convolutional neural network (CNN) architectures and demonstrated that it could correctly classify lesions between LM and AIMP on biopsy-confirmed RCM image stacks. We identified local z-projection (LZP) as a recent fast approach for projecting a 3D image into 2D while preserving information and achieved high-accuracy machine classification with minimal computational requirements.

Bedside Differentiation Between Benign and Malignant Pigmented Skin Tumours by Four Diagnostic Imaging Technologies - A Pilot Study

Fast diagnosis of suspicious pigmented skin lesions is imperative, but current bedside skin imaging technologies are either limited in penetration depth or resolution. Combining imaging methods is therefore highly relevant for skin cancer diagnostics. This pilot study evaluates the ability of optical coherence tomography, reflectance confocal microscopy, photoacoustic imaging and high-frequency ultrasound to differentiate malignant from benign pigmented skin lesions. A total of 41 pigmented skin tumours were scanned prior to excision. Morphologic features and blood vessel characteristics were analysed in reflectance confocal microscopy, optical coherence tomography, high-frequency ultrasound and photoacoustic imaging images and diagnostic accuracy assessed. Three novel photoacoustic imaging features, 7 reflectance confocal microscopy features and two optical coherence tomography features were detected with a high correlation to malignancy, diagnostic accuracy > 71%. No significant features were found in high-frequency ultrasound. Conclusively, optical coherence tomography, reflectance confocal microscopy and photoacoustic imaging in combination enables image-guided evaluation of suspicious pigmented skin tumours at the bedside. Combining these advanced techniques may help to diagnose skin cancer more efficiently.