AIMC Topic: Retrospective Studies

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Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

Scientific reports
We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National...

Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.

Scientific reports
This study aimed to explore potential risk factors for severe endometriosis and to develop a model to predict the risk of severe endometriosis. A total of 308 patients with endometriosis were analyzed. Least absolute shrinkage and selection operator ...

Machine learning-based multiparametric CT radiomics for predicting microvascular invasion before nephrectomy in clear cell renal cell carcinoma.

Abdominal radiology (New York)
PURPOSE: This study aimed to investigate the value of integrating computed tomography (CT)-based tumor radiomics features with clinical parameters for preoperative prediction of microvascular invasion (MVI) in clear cell renal cell carcinoma (ccRCC).

Deep learning reconstruction of diffusion-weighted imaging with single-shot echo-planar imaging in endometrial cancer: a comparison with multi-shot echo-planar imaging.

Abdominal radiology (New York)
PURPOSE: To evaluate the efficacy of deep learning reconstruction (DLR) in diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (SSEPI) for endometrial cancer, compared to multiplexed sensitivity-encoding (MUSE) DWI.

Habitat Radiomics and Deep Learning Features Based on CT for Predicting Lymphovascular Invasion in T1-stage Lung Adenocarcinoma: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used to predict lymphovascular invasion (LVI) in patients with T1-stage lung adenocarcinoma (LUAD), and compare its effectiveness to traditional radiomics ...

Prediction of early recurrence in primary central nervous system lymphoma based on multimodal MRI-based radiomics: A preliminary study.

European journal of radiology
OBJECTIVES: To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis.

Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

Academic radiology
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....

Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.

International journal of surgery (London, England)
BACKGROUND: Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LNM) in early gastric cancer (EGC), yet tools for prediction grounded in the Lauren classification rem...

Updated perspectives on visceral pleural invasion in non-small cell lung cancer: A propensity score-matched analysis of the SEER database.

Current problems in cancer
BACKGROUND: Visceral pleural invasion (VPI), including PL1 (the tumor invades beyond the elastic layer) and PL2 (the tumor extends to the surface of the visceral pleura), plays a crucial role in staging Non-Small Cell Lung Cancer (NSCLC). However, th...

Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

Circulation. Genomic and precision medicine
BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs treatment. The heart failure biomarker NT-proBNP (N-terminal pro-B-type natriuretic peptide) is used to...