AIMC Topic: Prognosis

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An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study.

European radiology
OBJECTIVES: To develop and validate an interpretable and generalized machine learning model using MRI for the individualized prediction of induction chemotherapy (ICT) response and survival in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

BMC cancer
BACKGROUND: Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. Howe...

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Scientific reports
Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue. Artificial intelligence (AI) technologies can extract a wide range of tumor features from large datasets helping in oncological decision-ma...

A Deep Reinforcement Learning-Based Feature Selection Method for Invasive Disease Event Prediction Using Imbalanced Follow-Up Data.

IEEE journal of biomedical and health informatics
The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction mod...

ContraSurv: Enhancing Prognostic Assessment of Medical Images via Data-Efficient Weakly Supervised Contrastive Learning.

IEEE journal of biomedical and health informatics
Prognostic assessment remains a critical challenge in medical research, often limited by the lack of well-labeled data. In this work, we introduce ContraSurv, a weakly-supervised learning framework based on contrastive learning, designed to enhance p...

Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Journal of cancer research and clinical oncology
INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplific...

Ultrasonic-Based Radiomics Signature With Machine Learning for Differentiating Prognostic Subsets of Pediatric Peripheral Neuroblastic Tumors: A Retrospective Study.

Ultrasound in medicine & biology
OBJECTIVE: To construct and select a better model based on ultrasonic-based radiomics features and clinical characteristics for prognostic subsets of pediatric neuroblastic tumors.

Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study.

Arthritis research & therapy
OBJECTIVE: The aim of this study was to investigate the correlation between age, red cell distribution width (RDW) levels, and 180-day and 1-year mortality in giant cell arteritis (GCA) patients hospitalized or admitted to the ICU.

Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.

Chinese medical journal
BACKGROUND: Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN a...

Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...