AIMC Topic: Melanoma, Cutaneous Malignant

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Automated assessment of skin histological tissue structures by artificial intelligence in cutaneous melanoma.

Pathology, research and practice
BACKGROUND: Prognostic histopathological features such as mitosis in melanoma are excluded from the staging systems due to inter-observer variability and time constraints. While digital pathology offers artificial intelligence-driven solutions, exist...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

The American Journal of dermatopathology
BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supported by immunohistochemical staining, is the gold standard for detecting LN metastases....

Skin cancer detection using dermoscopic images with convolutional neural network.

Scientific reports
Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and...

Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma.

Genes and immunity
The functions of immunosenescence are closely related to skin cutaneous melanoma (SKCM). The aim of this study is to uncover the characteristics of immunosenescence index (ISI) to identify novel biomarkers and potential targets for treatment. Firstly...

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

Archives of dermatological research
PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growth...

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.

Apoptosis : an international journal on programmed cell death
The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently...

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Microscopy research and technique
Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most seriou...

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.

Melanoma research
Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a tumor. ITH was involved in the cancer progression, aggressiveness, therapy resistance and cancer rec...

Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study.

Journal of cancer research and clinical oncology
BACKGROUND: Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.