Other Cancers

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients w...

The Role of AI in Lymphoma: An Update.

Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burkitt lymphomas predominate, while older populations more commonly experience subtypes such as diffuse large B-cell, follicular, marginal zone, and mantle cell lymphomas. Positron emission tomography/computed tomography (PET/CT) using [F] fluorodeoxyglu...

GraphSleepFormer: a multi-modal graph neural network for sleep staging in OSA patients.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder. Accurate sleep staging is one of the prerequisites in the study of sleep-related disorder...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditiona...

Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction.

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) ...

Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.

BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepat...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who...

A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.

BACKGROUND: Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosis. The Peritoneal Cancer Index (PCI) is used to ev...

Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer.

Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-s...

Innovative construction and application of bile duct organoids: Unraveling the complexity of bile duct diseases and potential therapeutic strategies.

The biliary system is crucial for liver function, regulating bile production, secretion, and transport. Dysfunctions within this system can lead to va...

Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.

. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion management method to reduce internal target volume marg...

Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography.

PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deform...

The accuracy of artificial intelligence in the diagnosis of soft tissue sarcoma: A systematic review and meta-analysis.

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth fa...

Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the in...

Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability.

BACKGROUND AND OBJECTIVE: Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often pro...

Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches.

OBJECTIVES: To evaluate the roles of oxidative balance score (OBS) in staging and mortality risk of cardiovascular-kidney-metabolic syndrome (CKM).

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by t...

Interstitial-guided automatic clinical tumor volume segmentation network for cervical cancer brachytherapy.

Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial brachytherapy cervical cancer. However, the promin...

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment pro...