Other Cancers

Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.

BACKGROUND: Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We developed a deep learning model for detection of melanocytes in resection margins of LM.

Towards key genes identification for breast cancer survival risk with neural network models.

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the...

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune checkpoint inhibitors have demonstrated promising t...

Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology.

Peritoneal washing cytology (CY) in patients with pancreatic cancer is mainly used for staging; however, it may also be used to evaluate the intraperi...

Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus ...

Cheminformatics analysis of indoleamine and tryptophan 2,3-dioxygenase inhibitors: A descriptor and fingerprint based machine learning approach to disclose selectivity measures.

Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are attractive drug targets for cancer immunotherapy. After disappointing resul...

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis Using Histopathological Images.

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diag...

Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis.

BACKGROUND: The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially maligna...

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal can...

Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study.

BACKGROUND: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to p...

Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.

OBJECTIVE: To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamo...

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonanc...

Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction.

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allo...

Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.

OBJECTIVE: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), define...

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine...

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.

PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year m...

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.

BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the ...

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.

BACKGROUND: Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cance...

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological ...

Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic sign...