AIMC Topic: Nomograms

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Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical validation.

Scientific reports
Primary myelofibrosis (PMF) is a heterogeneous bone marrow disorder, and substantial evidence indicates the involvement of inflammatory mediators in its progression. However, a diagnostic model based on inflammation-related genes has not yet been est...

Prognostic value of combining nutritional inflammatory index trajectories and tumor characteristics in cervical cancer.

BMC women's health
OBJECTIVE: This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.

Machine learning combined with body composition predicts surgical difficulty in mid-low rectal cancer surgery.

Annals of medicine
BACKGROUND: This study sought to identify critical body composition characteristics associated with surgical difficulty in Laparoscopic Total Mesorectal Excision (LaTME) and to develop and validate an interpretable machine learning model using body c...

Development and internal validation of a preoperative prediction model for postoperative pneumonia in lung cancer patients: a retrospective study.

BMC surgery
PURPOSE: To evaluate the postoperative pneumonia (POP) risk of patients with non-small cell lung cancer (NSCLC), identify influencing factors, develop a LASSO regression-based model to predict POP risk and identify critical influencing factors.

CT-based radiomics and deep learning models for predicting thyroid cartilage invasion and patient prognosis in laryngeal carcinoma.

Scientific reports
Accurate assessment of thyroid cartilage invasion is crucial for treatment decision-making and prognosis evaluation in laryngeal squamous cell carcinoma (LSCC). This study aimed to compare the performance of the radiomics and deep learning (DL) model...

MRI-based 2.5D deep learning and radiomics effectively predicted microvascular invasion and Ki-67 expression in hepatocellular carcinoma.

PloS one
OBJECTIVE: To develop and validate an integrated 2.5D deep learning (DL) and Radiomics model using gadoxetic acid-enhanced MRI hepatobiliary phase (HBP) images combined with clinical features for preoperative prediction of microvascular invasion (MVI...

Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...

Primary tumor resection: a new hope or an old illusion for patients with metastatic non-small cell lung neuroendocrine tumors?

World journal of surgical oncology
OBJECTIVES: This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may be...

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...

Ultrasound-based radiomics model for predicting axillary lymph node metastasis of breast cancer.

BMC medical imaging
OBJECTIVE: This study aims to explore the impact of different ROI delineation strategies on the axillary lymph nodes metastasis (ALNM) prediction model by analyzing two-dimensional ultrasound images of lymph nodes. In addition, we integrated clinical...