Other Cancers

Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.

Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and whole transcriptome sequencing on plasma and leukocytes with advanced machine learning techniques to satisfy all three clinical testing needs on one pl...

Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.

PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial.

Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features and clinical parameters.

BACKGROUND: Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of cen...

Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.

OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an import...

The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.

BACKGROUND AND AIMS: More cases of thyroid micro-nodules have been diagnosed annually in recent years because of advancements in diagnostic technologi...

Can mutation abundance assess the biological behavior of BRAF-positive papillary thyroid carcinoma?

BACKGROUND: BRAF mutation is the most common genetic change in papillary thyroid carcinoma (PTC). Nevertheless, the association between BRAF mutation ...

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it is time and tissue consuming, and highly dependent...

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions.

OBJECTIVE: To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiolog...

2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing ...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tu...

Development and validation of a small-sample machine learning model to predict 5-year overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy metastasis, high recurrence rate, and significant mo...

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal adenocarcinoma (PDAC), significantly contributing to...

Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions.

BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems, presenting opportunities to improve clinical outcomes. In t...

Taking the Guess Work Out of Endoscopic Polyp Measurement: From Traditional Methods to AI.

Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for...

A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.

AIMS: Adrenal incidentalomas (AIs) encompass a wide range of clinical entities, from incidental benign neoplasms that need to be monitored to aggressi...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Thoracic staging of lung cancers by FDG-PET/CT: impact of artificial intelligence on the detection of associated pulmonary nodules.

This study focuses on automating the classification of certain thoracic lung cancer stages in 3D FDG-PET/CT images according to the 9th Edition of the...

Boosting Checkpoint Blockade Immunotherapy with T Cell Membrane Redox Homeostasis Regulation and Deep Learning Enhanced NIR-II Imaging.

Immune checkpoint blockade therapy has revolutionized clinical oncotherapy by intercepting immunosuppressive pathways in tumor tissues. Nevertheless, ...

Artificial Intelligence-Based prediction model for surgical site infection in metastatic spinal disease: a multicenter development and validation study.

BACKGROUND: Treatment of metastatic spinal disease often involves surgical intervention; however, surgical site infections (SSI) pose a great challeng...

Raman spectroscopy in tandem with machine learning - based decision logic methods for characterization and detection of primary precancerous and cancerous cells.

Early cancer detection improves patient outcomes, but most Raman spectroscopy research has focused on discriminating between normal and malignant cell...