Automated detection of actinic keratoses in clinical photographs

Insights, Advantages, and Barriers of Teledermatology vs. Face-to-Face Dermatology for the Diagnosis and Follow-Up of Non-Melanoma Skin Cancer: A Systematic Review

BACKGROUND

Teledermatology is employed in the diagnosis and follow-up of skin cancer and its use was intensified during and after the COVID-19 pandemic. At the same time, demographic changes result in an overall increase in non-melanoma skin cancer and skin precancerous lesions. The aim of this study was to elucidate the role of teledermatology in comparison to conventional face-to-face dermatology for such lesions and determine the advantages and limitations of this workflow for patients and physicians.

METHODS

Research was performed using relevant keywords in MEDLINE and CENTRAL. Relevant articles were chosen following a predetermined standardized extraction form.

RESULTS

Diagnostic accuracy and interrater/intrarater agreement can be considered comparable-although lower-than in-person consultation. Improvement of particular features such as image quality, medical history availability, and teledermoscopy can further increase accuracy. Further aspects of limitations and advantages (mean time-to-assessment, time-to-treatment, cost-effectiveness) are discussed.

CONCLUSIONS

Teledermatology has comparable diagnostic accuracy with face-to-face dermatology and can be utilized both for the effective triage of non-melanocytic epithelial tumors and precancerous lesions, as well as the follow-up. Easy access to dermatologic consultation with shorter mean times to diagnostic biopsy and/or treatment coupled with cost-effectiveness could compensate for the lower sensitivity of teledermatology and offer easier access to medical care to the affected populations.

Where Artificial Intelligence Can Take Us in the Management and Understanding of Cancerization Fields

Squamous cell carcinoma and its precursor lesion actinic keratosis are often found together in areas of skin chronically exposed to sun, otherwise called cancerisation fields. The clinical assessment of cancerisation fields and the correct diagnosis of lesions within these fields is usually challenging for dermatologists. The recent adoption of skin cancer diagnostic imaging techniques, particularly LC-OCT, helps clinicians in guiding treatment decisions of cancerization fields in a non-invasive way. The combination of artificial intelligence and non-invasive skin imaging opens up many possibilities as AI can perform tasks impossible for humans in a reasonable amount of time. In this text we review past examples of the application of AI to dermatological images for actinic keratosis/squamous cell carcinoma diagnosis, and we discuss about the prospects of the application of AI for the characterization and management of cancerization fields.

The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild

AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving upon both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.

Inter- and Intra-physician variation in quantifying actinic keratosis skin photodamage

We investigated the variations in physician evaluation of skin photodamage based on a published photodamage scale. Of interest is the utility of a 10-level scale ranging from none and mild photodamage to actinic keratosis (AK). The dorsal forearms of 55 adult subjects with various amounts of photodamage were considered. Each forearm was independently evaluated by 15 board-certified dermatologists according to the Global Assessment Severity Scale ranging from 0 (less severe) to 9 (the most progressed stage of skin damage). Dermatologists rated the levels of photodamage based upon the photographs in blinded fashion. Results show substantial disagreement amongst the dermatologists on the severity of photodamage. Our results indicate that ratings could be more consistent if using a scale of less levels (5-levels or 3-levels). Ultimately, clinicians can use this knowledge to provide better interpretation of inter-rater evaluations and provide more reliable assessment and frequent monitoring of high-risk populations.

Vascular patterns in basal cell carcinoma: Dermoscopic, confocal and histopathological perspectives

Basal cell carcinoma (BCC) is the most prevalent skin cancer in the Caucasian population. A variety of different phenotypic presentations of BCC are possible. Although BCCs rarely metastasize, these tumors commonly destroy underlying tissues and should therefore be treated promptly. As vascular formation and angiogenesis are indicators of tumor development and progression, the presence of blood vessels, their morphology and architecture are important markers in skin lesions, providing critical information towards pathogenesis and diagnosis. BCC commonly lacks pigmentation, therefore it is important to emphasize the usefulness of vascular feature detection, recognition, quantification and interpretation. To answer the question of whether vascular patterns observed on dermoscopy, reflectance confocal microscopy (RCM) and histopathology might reflect the biologic behavior of BCCs, we undertook this review article. Several studies have sought, by various means, to identify vascular features associated with the more aggressive BCC phenotypes. Dermoscopic vascular pattern assessment can facilitate diagnostic discrimination between BCC subtypes, more aggressive BCCs displaying less or no pink coloration and a relative absence of central tumor vessels. RCM, a novel, non-invasive imaging technique, allows for the quantification of blood vessel size, density, and flow intensity in BCCs. BCCs are distinguished on RCM chiefly by vessels that branch and intertwine between neoplastic aggregates, a pattern strongly reflecting tumor neo-angiogenesis. The analysis of these vascular morphological and distribution patterns can provide further support in the diagnosis, assessment, or monitoring of BCCs. Histopathology shows significantly higher microvessel densities in the peritumoral stroma of BCCs, when compared to normal skin or benign tumors. This angiogenic response in the stroma is associated with local aggressiveness, therefore the quantification of peritumoralmicrovessels may further assist with tumor evaluation. How dermoscopy and RCM vascular patterns in BCC correlate with histopathological subtype and thus help in discriminating aggressive subtypes definitely deserves further investigation.

Late fusion of deep and shallow features to improve discrimination of actinic keratosis from normal skin using clinical photography

BACKGROUND

Actinic keratosis (AK) is a common premalignant skin lesion that can potentially progress to squamous cell carcinoma. Appropriate long-term management of AK requires close patient monitoring in addition to therapeutic interventions. Computer-aided diagnostic systems based on clinical photography might evolve in the future into valuable adjuncts to AK patient management. The present study proposes a late fusion approach of color-texture features (shallow features) and deep features extracted from pre-trained convolutional neural networks (CNN) to boost AK detection accuracy on clinical photographs.

MATERIALS AND METHODS

System uses a sliding rectangular window of 50 × 50 pixels and a classifier that assigns the window region to either the AK or the healthy skin class. 6010 and 13 915 cropped regions of interest (ROI) of 50 × 50 pixels of AK and healthy skin, respectively, from 22 patients were used for system implementation. Different support vector machine (SVM) classifiers employing shallow or deep features and their late fusion using the max rule at decision level were compared with the McNemar test and Yule's Q-statistic.

RESULTS

Support vector machine classifiers based on deep and shallow features exhibited overall competitive performances with complementary improvements in detection accuracy. Late fusion yielded significant improvement (6%) in both sensitivity (87%) and specificity (86%) compared to single classifier performance.

CONCLUSION

The parallel improvement of sensitivity and specificity is encouraging, demonstrating the potential use of our system in evaluating AK burden. The latter might be of value in future clinical studies for the comparison of field-directed treatment interventions.