Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

BACKGROUND: Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. OBJECTIVES: Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. METHODS: We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. RESULTS: A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. CONCLUSIONS: Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

Dermoscopy to differentiate clinically similar inflammatory and neoplastic skin lesions.

INTRODUCTION: Over the few last decades, dermoscopy has become an invaluable and popular imaging technique that complements the diagnostic armamentarium of dermatologists, being employed for both tumors and inflammatory diseases. Whereas distinction between neoplastic and inflammatory lesions is often straightforward based on clinical data, there are some scenarios that may be troublesome, e.g., solitary inflammatory lesions or tumors superimposed to a widespread inflammatory condition that may share macroscopic morphological findings. EVIDENCE ACQUISITION: We reviewed the literature to identify dermoscopic clues to support the differential diagnosis of clinically similar inflammatory and neoplastic skin lesions, also providing the histological background of such dermoscopic points of differentiation. EVIDENCE SYNTHESIS: Dermoscopic differentiating features were identified for 12 relatively common challenging scenarios, including Bowen's disease and basal cell carcinoma vs. psoriasis and dermatitis, erythroplasia of Queyrat vs. inflammatory balanitis, mammary and extramammary Paget's disease vs. inflammatory mimickers, actinic keratoses vs. discoid lupus erythematosus, squamous cell carcinoma vs. hypertrophic lichen planus and lichen simplex chronicus, actinic cheilitis vs. inflammatory cheilitis, keratoacanthomas vs. prurigo nodularis, nodular lymphomas vs. pseudolymphomas and inflammatory mimickers, mycosis fungoides vs. parapsoriasis and inflammatory mimickers, angiosarcoma vs granuloma faciale, and Kaposi sarcoma vs pseudo-Kaposi. CONCLUSIONS: Dermoscopy may be of aid in differentiating clinically similar inflammatory and neoplastic skin lesions.

Diagnosis of invasive cutaneous squamous cell carcinoma, imaging and staging.

The assessment of patients with a lesion raising the suspicion of an invasive cutaneous squamous cell carcinoma (cSCC) is a frequent clinical scenario. The management of patients with cSCC is a multistep approach, starting with the correct diagnosis. The two main diagnostic goals are to differentiate from other possible diagnoses and correctly recognize the lesion as cSCC, and then to determine the tumor spread (perform staging), that is if the patient has a common primary cSCC or a locally advanced cSCC, or a metastatic cSCC (with in-transit, regional lymph nodal, or rarely distant metastasis). The multistep diagnostic approach begins with the clinical characteristics of the primary cSCC, it is complemented with features with dermoscopy and, if available, reflectance confocal microscopy and is confirmed with histopathology. The tumor spread is assessed by physical examination and, in some cases, ultrasound and/or computed tomography or magnetic resonance imaging, mainly to investigate for regional lymph node metastasis or for local infiltration into deeper structures. In the last step, the clinical, histologic and radiologic findings are incorporated into staging systems.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.

Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

Onychocytic Matricoma: A Clinical, Dermoscopic, and Pathological Analysis of 14 Cases.

ABSTRACT: Onychocytic matricoma (OCM) is a benign neoplasm of the nail matrix. Only 18 cases of this tumor have been reported in the literature to date. We retrospectively analyzed the clinical features of 14 patients with OCM. The most common clinical feature was longitudinal xanthopachyonychia (n = 9), followed by longitudinal leukopachyonychia (=3) and longitudinal pachymelanonychia (n = 2). The most common clinical findings identified following dermoscopy and analysis at high magnification of classical photographs were free-edge thickening of the nail plate without pitting (n = 14), longitudinal ridging (n = 7), round white clods (n = 7), white dots (n = 7), and filiform hemorrhages (n = 7), followed by oval and linear white clods (n = 5), fuzzy lateral border (n = 5), and red-purple blood clods (n = 3). Nail clipping histopathology showed a thickened nail plate with multiple, small, round-to-oval spaces. The tumor expressed immunopositivity for LEF-1. Dermoscopy of the nail plate and nail clipping histology provides useful information with regards to the differential diagnosis with subungual squamous cell carcinoma and nail melanoma. Ex vivo-in vivo correlation facilitates a better dermoscopic assessment of this unique underrecognized disease. However, the differential diagnosis between OCM and onychocytic carcinoma requires biopsy of the tumor. LEF-1 as an onychogenic marker can be used to resolve the differential diagnosis between OCM and subungual longitudinal acanthoma/seborrheic keratosis.

Clinicopathological, dermoscopic features and circumstances of diagnosis of amelanotic or hypomelanotic melanoma: A prospective multicentric study in the French private medical sector.

BACKGROUND: Amelanotic or hypomelanotic melanomas (AHM) are difficult to diagnose, and are often diagnosed late, with a high Breslow index and a poor prognosis. PATIENTS AND METHODS: A total of 226 volunteer dermatologists consulting in private practice in France completed an online form for each new histologically proven case of melanoma diagnosed at their clinic in 2020. This anonymised survey collected data on the clinical, dermoscopic, and histological features of melanoma, as well as the circumstances of diagnosis and initial management. A group of 145 AHM was single out and compared to the 1503 pigmented melanomas (PM) from the same cohort. RESULTS: 1503 pigmented melanomas (PM) and 145 AHM (8.8% of these melanomas) were identified and included. In the AHM group, the mean age at diagnosis was 65 ±â€¯16 years, with no significant difference from the PM control group. AHM were not predominantly on the face and neck area, and there were no differences based on gender. Warning signs (local progression and bleeding) were significantly more frequent in the AHM group than in the PM group. AHM were more frequently ulcerated and nodular, with a higher median Breslow thickness than in the PM group (1.56 vs. 0.5 mm), and mitoses were more frequent. Dermoscopy was widely used and proved useful for distinguishing benign lesions, and for highlighting the vascular polymorphous pattern of malignant lesions. Patients noticed the suspicious lesion themselves in most cases of AHM (73.2%), as opposed to their general practitioner (17.2%) or entourage (9.5%). A total body skin examination enabled detection of 19.3% of AHM and 21.3% of PM where the patient consulted for another lesion, or for an unrelated reason. CONCLUSION: AHM are difficult to diagnose for the clinician because of the paucity or absence of pigmentary criteria. Knowledge of dermoscopic vascular patterns is critical and could help reduce the median Breslow index of AHM at the time of detection. Self-examination of the skin should be encouraged, and simple algorithms for earlier detection of skin cancers should be promoted among health professionals and the general population.

Dermatoscopy and reflectance confocal microscopy for basal cell carcinoma diagnosis and diagnosis prediction score: A prospective and multicenter study on 1005 lesions.

BACKGROUND: Basal cell carcinoma (BCC) is usually diagnosed by clinical and dermatoscopy examination, but diagnostic accuracy may be suboptimal. Reflectance confocal microscopy (RCM) imaging increases skin cancer diagnostic accuracy. OBJECTIVE: To evaluate additional benefit in diagnostic accuracy of handheld RCM in a prospective controlled clinical setting. METHODS: A prospective, multicenter study in 3 skin cancer reference centers in Italy enrolling consecutive lesions with clinical-dermatoscopic suspicion of BCC (ClinicalTrials.gov: NCT04789421). RESULTS: A total of 1005 lesions were included, of which 474 histopathologically confirmed versus 531 diagnosed by clinical-dermatoscopic-RCM correlation, confirmed with 2 years of follow-up. Specifically, 740 were confirmed BCCs. Sensitivity and specificity for dermatoscopy alone was 93.2% (95% CI, 91.2-94.9) and 51.7% (95% CI, 45.5-57.9); positive predictive value was 84.4 (95% CI, 81.7-86.8) and negative predictive value 73.3 (95% CI, 66.3-79.5). Adjunctive RCM reported higher rates: 97.8 (95% CI, 96.5-98.8) sensitivity and 86.8 (95% CI, 82.1-90.6) specificity, with positive predictive value of 95.4 (95% CI, 93.6-96.8) and negative predictive value 93.5 (95% CI, 89.7-96.2). LIMITATIONS: Study conducted in a single country. CONCLUSIONS: Adjunctive handheld RCM assessment of lesions clinically suspicious for BCC permits higher diagnostic accuracy with minimal false negative lesions.

Dynamic optical coherence tomography evaluation in locally advanced basal cell carcinoma during sonidegib treatment.

BACKGROUND: Basal cell carcinoma (BCC) is the most common cancer in the Caucasian population. It has a multifactorial pathogenesis, in which constitutive activation of the Sonic Hedgehog signalling (SHH) pathway (via mutations in PTCH1 or SMO genes) represents by far the most common genetic aberration. The introduction of vismodegib and sonidegib, two SHH pathway inhibitors, changed the therapeutic approach of locally advanced and metastatic BCCs. EADO's (European Association of Dermato-Oncology) new staging system refers to these as 'difficult-to-treat' BCCs. OBJECTIVE: The aim was to evaluate sonidegib's effectiveness in patients affected by difficult-to-treat BCCs by using non-invasive diagnostic techniques. METHODS: We retrospectively evaluated 14 patients (4 females, 10 males; mean age 77 ± 11 years) affected by difficult-to-treat BCCs treated with oral sonidegib 200 mg/day that were followed with total body videodermoscopy (V-Track, Vidix 4.0) and dynamic optical coherence tomography (D-OCT, VivoSight Dx) since May 2022. Considering the risk of rhabdomyolysis routine blood tests, especially for creatine kinase concentrations, were performed. All treated patients were inserted in the BasoCare database, which aims to offer support to patients taking sonidegib. Complete and partial responses were evaluated by the overall reduction of the number of lesions and their individual sizes. Safety was evaluated by assessing the occurrence and severity of adverse reactions. RESULTS: Eighty per cent achieved complete clearance and 75% reduction of diameter. D-OCT scans performed at every follow-up showed concordance with clinical appearance and demonstrated reduction of hyporeflective structures, that is, islets of tumour cells and overall improvement of morphology. CONCLUSION: Sonidegib can be considered an effective treatment option in cases where surgery or radiotherapy would be unfeasible or has previously failed, although pigmented lesions did not show complete clearance, suggesting that there are factors other than the SHH pathway involved in tumour growth. Videodermoscopy and D-OCT were useful in the quick and seamless follow-up of lesions and added valuable information in assessing efficacy.