AIMC Topic: Nomograms
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Machine learning-based integration develops a multiple programmed cell death signature for predicting the clinical outcome and drug sensitivity in colorectal cancer.
Anti-cancer drugs
Aug 9, 2024
Tumorigenesis and treatment are closely associated with various programmed cell death (PCD) patterns. However, the coregulatory role of multiple PCD patterns in colorectal cancer (CRC) remains unknown. In this study, we developed a multiple PCD index...
Development and validation of a nomogram to predict impacted ureteral stones via machine learning.
Minerva urology and nephrology
Aug 2, 2024
BACKGROUND: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.
Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.
Medical molecular morphology
Aug 1, 2024
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients...
Identification of Key Efferocytosis-Related Genes and Mechanisms in Diabetic Retinopathy.
Molecular biotechnology
Jul 31, 2024
This study aimed to explore the key efferocytosis-related genes in diabetic retinopathy (DR) and their regulatory mechanisms. Public DR-related gene expression datasets, GSE160306 (training) and GSE60436 (validation), were downloaded. Differentially ...
Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.
BMC medical imaging
Jul 26, 2024
BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).
A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.
Journal of cancer research and clinical oncology
Jul 25, 2024
PURPOSE: Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.
Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.
BMC medical imaging
Jul 19, 2024
OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular tre...
Preoperative Contrast-Enhanced CT-Based Deep Learning Radiomics Model for Distinguishing Retroperitoneal Lipomas and Well‑Differentiated Liposarcomas.
Academic radiology
Jul 14, 2024
RATIONALE AND OBJECTIVES: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroper...
A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years.
Frontiers in endocrinology
Jul 12, 2024
OBJECTIVE: The objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years.
Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.
Cancer science
Jul 11, 2024
The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarker...
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