Skin Cancer

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.

Melanoma Skin Cancer Detection Using Recent Deep Learning Models.

Melanoma is considered as one of the world's deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the most effective melanoma detection techniques. This study presents the use of recent deep CNN approaches to detect melanoma skin cancer and investigate ...

RoBoT: a robust Bayesian hypothesis testing method for basket trials.

A basket trial in oncology encompasses multiple "baskets" that simultaneously assess one treatment in multiple cancer types or subtypes. It is well-re...

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studie...

Interest in artificial intelligence for the diagnosis of non-melanoma skin cancer: a survey among French general practitioners.

General practitioners (GPs) are playing a key role in skin cancer screening. Non-melanoma skin cancer is frequent and difficult to diagnose. We aimed ...

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient progno...

Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged ...

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience...

Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer.

In recent clinical practice the molecular diagnostics have been significantly empowered and upgraded by the use of Artificial Intelligence and its ass...

Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of sus...

Role of Intralesional Vitamin D3 in the Treatment of Cutaneous Warts.

BACKGROUND: Cutaneous warts are common benign skin lesions caused by human papillomavirus. Various treatment options are available for these but immun...

Dermoscopic Features of Giant Molluscum Contagiosum in a Patient with Acquired Immunodeficiency Syndrome.

Giant molluscum contagiosum (MC) is a peculiar variant of the disease with the presence of multiple or single lesions larger than 5 mm. In contrast to...

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

IMPORTANCE: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic...

[Determination of dacarbazine in the urine of mice with melanoma by high performance liquid chromatography].

Dacarbazine (DTIC) is a first-line chemotherapy drug that is widely used in clinical practice for malignant melanoma. DTIC is metabolized by the liver...

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from mali...

Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier.

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the ...

Elucidating the interaction dynamics between microswimmer body and immune system for medical microrobots.

The structural design parameters of a medical microrobot, such as the morphology and surface chemistry, should aim to minimize any physical interactio...

The value of convolutional neural networks in the diagnosis of melanoma simulators.

Adaptive back-stepping cancer control using Legendre polynomials.

Here, a model-free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the populatio...

Comparative Study in Efficacy and Safety of Intralesional Injections of Vitamin D3, Measles Rubella (MR) Vaccine, and Purified Protein Derivative (PPD) in the Management of Cutaneous Warts.

INTRODUCTION: Cutaneous warts caused by human papillomavirus are the most common dermatological diseases being contagious, recurrent, and recalcitrant...