AIMC Topic: Survival Rate

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Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

Neuro-oncology
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Prognostic value of circadian rhythm-associated genes in breast cancer.

World journal of surgical oncology
OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circadian rhythms, as well as the timing of therapeutic interventions, influence the response to radiation...

Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
OBJECTIVE: The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to p...

Using Machine Learning to Predict Survival in Patients with Metastatic Castration-Resistant Prostate Cancer.

Studies in health technology and informatics
Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers for predicting survival in patients with metastatic castration-resistant prostate cancer (mCRPC) sti...

Machine learning model using immune indicators to predict outcomes in early liver cancer.

World journal of gastroenterology
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery.

Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.

Neuro-oncology
BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor f...

Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database.

Journal of orthopaedic surgery (Hong Kong)
The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has better overall performance than single computational approaches for clinical decision. The primary objective was to select prognostic variables and d...

Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer.

Medicine
The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model. This study was aimed to evaluate the performance of machine learning algorithm in predicting survival rat...

[CLINICAL EVALUATION OF THERAPEUTIC EFFECT PREDICTORS IN PEMBROLIZUMAB FOR ADVANCED UROTHELIAL CANCER].

Nihon Hinyokika Gakkai zasshi. The japanese journal of urology
(Purpose) We performed a clinical retrospective study on the evaluation of pembrolizumab treatment results for advanced urothelial cancer in our hospital. (Materials and Methods) Twenty-seven patients diagnosed with advanced or metastatic urothelial ...

Applications of deep learning models in precision prediction of survival rates for heart failure patients.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12...