AI Medical Compendium Journal:
Cancer letters

Showing 1 to 10 of 15 articles

Generating research hypotheses to overcome key challenges in the early diagnosis of colorectal cancer - Future application of AI.

Cancer letters
We intend to explore the capability of ChatGPT 4.0 in generating innovative research hypotheses to address key challenges in the early diagnosis of colorectal cancer (CRC). We asked ChatGPT to generate hypotheses focusing on three main challenges: im...

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications.

Cancer letters
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal t...

Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.

Cancer letters
Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically presents with poor prognosis. Lactate is a crucial metabolite in cancer and significantly impacts tumor biology. Here, we aimed to construct a lactate-re...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

Cancer letters
GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies have reduced the mortality rates for GI malignancies, underscoring the main role of early detection ...

Integrating machine learning-predicted circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in metastatic breast cancer: A proof of principle study on endocrine resistance profiling.

Cancer letters
The study explored endocrine resistance by leveraging machine learning to establish the prognostic stratification of predicted Circulating tumor cells (CTCs), assessing its integration with circulating tumor DNA (ctDNA) features and contextually eval...

Personalized prediction of postoperative complication and survival among Colorectal Liver Metastases Patients Receiving Simultaneous Resection using machine learning approaches: A multi-center study.

Cancer letters
BACKGROUND: To predict clinical important outcomes for colorectal liver metastases (CRLM) patients receiving colorectal resection with simultaneous liver resection by integrating demographic, clinical, laboratory, and genetic data.

Artificial intelligence in intestinal polyp and colorectal cancer prediction.

Cancer letters
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic a...

CD147-specific chimeric antigen receptor T cells effectively inhibit T cell acute lymphoblastic leukemia.

Cancer letters
T cell acute lymphoblastic leukemia (T-ALL) is invasive and heterogeneous, and existing therapies are sometimes unsuccessful. Chimeric antigen receptor (CAR) T cell therapy is a breakthrough tumor treatment method, particularly for B cell acute lymph...

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Cancer letters
Despite numerous advances in cancer radiotherapy, tumor radioresistance remain one of the major challenges limiting treatment efficacy of radiotherapy. Conventional strategies to overcome radioresistance involve understanding the underpinning molecul...

Machine Learning in oncology: A clinical appraisal.

Cancer letters
Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have ...