AIMC Topic: Retrospective Studies

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Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.

Respiratory research
BACKGROUND: To evaluate the clinical applicability of deep learning (DL) models based on automatic segmentation in preoperatively predicting tumor spread through air spaces (STAS) in peripheral stage I lung adenocarcinoma (LUAD).

Hand X-rays findings and a disease screening for Turner syndrome through deep learning model.

BMC pediatrics
BACKGROUND: Turner syndrome (TS) is one of the important causes of short stature in girls, but there are cases of misdiagnosis and missed diagnosis in clinical practice. Our aim is to analyze the hand skeletal characteristics of TS patients and estab...

The value of radiomics and deep learning based on PET/CT in predicting perineural nerve invasion in rectal cancer.

Abdominal radiology (New York)
OBJECTIVE: The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer.

The Application of Machine Learning Models to Predict Stillbirths.

Medicina (Kaunas, Lithuania)
: This study aims to evaluate the predictive value of comprehensive data obtained in obstetric clinics for the detection of stillbirth and the predictive ability set of machine learning models for stillbirth. : The study retrospectively included all ...

Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.

BMC cancer
BACKGROUND AND OBJECTIVE: Spread through air spaces (STAS) is an important factor in determining the aggressiveness and recurrence risk of lung cancer, especially in early-stage adenocarcinoma. Preoperative identification of STAS is crucial for optim...

Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data.

Abdominal radiology (New York)
BACKGROUND: To explore the predictive value of radiomics features extracted from anatomical ROIs in differentiating the International Society of Urological Pathology (ISUP) grading in prostate cancer patients.