Other Cancers

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there ar...

Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images.

BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to accurately predict the status of KRAS, NRAS, and BRAF.

Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study.

BACKGROUND: To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malign...

A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.

Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identi...

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease a...

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accura...

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.

INTRODUCTION: Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator ...

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtyp...

Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.

PURPOSE: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papilla...

Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis.

BACKGROUND: Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of ar...

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demand...

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders.

Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing prope...

Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.

OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis ...

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.

Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bl...

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer.

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The applica...

Biomimetic piezoelectric nanomaterial-modified oral microrobots for targeted catalytic and immunotherapy of colorectal cancer.

Lactic acid (LA) accumulation in the tumor microenvironment poses notable challenges to effective tumor immunotherapy. Here, an intelligent tumor trea...

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤...

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors ...

Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology.

INTRODUCTION: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences ...

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in t...