AIMC Topic: Prognosis

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Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine.

International journal of oncology
Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combinati...

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study.

International journal of surgery (London, England)
OBJECTIVES: Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now.

Cuproptosis facilitates immune activation but promotes immune escape, and a machine learning-based cuproptosis-related signature is identified for predicting prognosis and immunotherapy response of gliomas.

CNS neuroscience & therapeutics
AIMS: Cell death, except for cuproptosis, in gliomas has been extensively studied, providing novel targets for immunotherapy by reshaping the tumor immune microenvironment through multiple mechanisms. This study aimed to explore the effect of cupropt...

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Journal of cancer research and clinical oncology
BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adeno...

AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes.

Urology
OBJECTIVE: To determine whether we can surpass the traditional R.E.N.A.L. nephrometry score (H-score) prediction ability of pathologic outcomes by creating artificial intelligence (AI)-generated R.E.N.A.L.+ score (AI+ score) with continuous rather th...

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application.

Seminars in cancer biology
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex ...

An AI Predictive Model to Determine Who Benefits from ADT with Radiation: Working Smarter, Not Harder.

NEJM evidence
Whether you are a surgical, medical, or radiation oncologist, the care goals remain the same, that is, achieving a durable treatment response. For patients with localized intermediate-risk prostate cancer undergoing radiation treatment, identifying t...

Prognostic impact of palpable prostate tumors on disease progression after robot-assisted radical prostatectomy: a single-center experience.

Journal of robotic surgery
OBJECTIVE: This study aimed to evaluate the impact of palpable prostate tumors on digital rectal exam (DRE) on the disease progression of prostate cancer (PCa) treated with RARP surgery in a tertiary referral center.

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

Stroke
BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about...

Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review.

Journal of gastrointestinal cancer
PURPOSE: Gastric cancer is an important health burden characterized by high prevalence and mortality rate. Upper gastrointestinal endoscopy coupled with biopsy is the primary means in which gastric cancer is diagnosed, and most of machine learning (M...