AIMC Topic: Retrospective Studies

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Interpretable Prognostic Modeling for Postoperative Pancreatic Cancer Using Multi-machine Learning and Habitat Radiomics: A Multi-center Study.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate risk stratification is critical for guiding personalized treatment in resectable pancreatic cancer (RPC). This retrospective study assessed the utility of habitat radiomics for predicting recurrence-free survival (R...

Prediction of EGFR Mutations in Lung Adenocarcinoma via CT Images: A Comparative Study of Intratumoral and Peritumoral Radiomics, Deep Learning, and Fusion Models.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to analyze the intratumoral and peritumoral characteristics of lung adenocarcinoma patients on the basis of chest CT images via radiomic and deep learning methods and to develop and validate a multimodel fusi...

Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).

International ophthalmology
PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

Medical physics
BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resec...

Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.

European journal of medical research
OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality predic...

Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.

BMC pregnancy and childbirth
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk as...

Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

BMC medical imaging
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...

Surgical and radiological outcomes of giant cell tumor of the bone: prognostic value of Campanacci grading and selective use of denosumab.

Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
BACKGROUND: Advancements in diagnostic and therapeutic modalities for giant cell tumors of bone (GCTB) have introduced molecular and radiological tools that refine clinical decision-making. H3.3 G34W immunohistochemical staining has become a routine ...

Brain tissue biomarker impact bone age in central precocious puberty more than hormones: a quantitative synthetic magnetic resonance study.

Japanese journal of radiology
OBJECTIVE: To investigate which brain tissue component volume (BTCV) biomarkers may be more effective than hormones in influencing bone age development in central precocious puberty (CPP).