Python technology and its applications in radiomics DOI

Yun-Chuan Xian,

Bao-Lei Zhang

New discovery., Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: Dec. 10, 2024

Python, developed by Guido van Rossum, is favored for its simplicity and extensive ecosystem of libraries, which facilitate efficient coding integration with other programming languages. Here, we aim to explore summarize the role Python in radiomics, a field focused on extracting analyzing quantitative features from medical imaging improve disease characterization treatment evaluation. Radiomics addresses complexities tumor heterogeneity transforming data modalities such as computed tomography (CT), magnetic resonance (MRI), positron emission (PET) into actionable insights, often using statistical methods machine learning techniques. Its primary applications include differentiating between benign malignant tumors predicting outcomes, etc. integral several stages including image acquisition, region interest (ROI) segmentation, feature extraction, analysis. By utilizing libraries PyRadiomics Scikit-learn, researchers can significantly enhance accuracy efficiency their analyses. Looking forward, holds considerable promise especially ongoing advancements big data. However, challenges standardization, model interpretability, patient privacy protection must be addressed fully unlock potential improving diagnostic precision outcomes.

Language: Английский

Correlation Analysis and Construction of a Predictive Model Between Contrast-Enhanced Ultrasound Features and the Risk of Recurrence in Granulomatous Mastitis DOI

Liju Ma,

Ping Du,

Xufeng Sun

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
A combined radiomics and clinical model for preoperative differentiation of intrahepatic cholangiocarcinoma and intrahepatic bile duct stones with cholangitis: a machine learning approach DOI Creative Commons
Hongwei Qian, Yanhua Huang,

Yanbin Dong

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: March 17, 2025

This study aimed to develop and validate a predictive model integrating radiomics features clinical variables differentiate intrahepatic bile duct stones with cholangitis (IBDS-IL) from cholangiocarcinoma (ICC) preoperatively, as accurate distinction is crucial for determining appropriate treatment strategies. A total of 169 patients (97 IBDS-IL 72 ICC) who underwent surgical resection were retrospectively analyzed. Radiomics extracted ultrasound images, significant differences between groups identified. Feature selection was performed using LASSO regression recursive feature elimination (RFE). The model, combined constructed evaluated the area under curve (AUC), calibration curves, decision analysis (DCA), SHAP analysis. achieved an AUC 0.962, 0.861. Score variables, demonstrated highest performance 0.988, significantly outperforming (p < 0.05). Calibration curves showed excellent agreement predicted observed outcomes, Hosmer-Lemeshow test confirmed good fit = 0.998). DCA revealed that provided greatest benefit across wide range threshold probabilities. identified most contributor, complemented by abdominal pain liver atrophy. data offers powerful reliable tool preoperative differentiation ICC. Its superior interpretability highlight its potential improving diagnostic accuracy guiding decision-making. Further validation in larger, multicenter datasets warranted confirm generalizability.

Language: Английский

Citations

0
CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII DOI
Burak Koçak, Andrea Ponsiglione, Arnaldo Stanzione

et al.

European Journal of Radiology, Journal Year: 2024, Volume and Issue: 181, P. 111788 - 111788

Published: Oct. 14, 2024

Language: Английский

Citations

1
Python technology and its applications in radiomics DOI

Yun-Chuan Xian,

Bao-Lei Zhang

New discovery., Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: Dec. 10, 2024

Python, developed by Guido van Rossum, is favored for its simplicity and extensive ecosystem of libraries, which facilitate efficient coding integration with other programming languages. Here, we aim to explore summarize the role Python in radiomics, a field focused on extracting analyzing quantitative features from medical imaging improve disease characterization treatment evaluation. Radiomics addresses complexities tumor heterogeneity transforming data modalities such as computed tomography (CT), magnetic resonance (MRI), positron emission (PET) into actionable insights, often using statistical methods machine learning techniques. Its primary applications include differentiating between benign malignant tumors predicting outcomes, etc. integral several stages including image acquisition, region interest (ROI) segmentation, feature extraction, analysis. By utilizing libraries PyRadiomics Scikit-learn, researchers can significantly enhance accuracy efficiency their analyses. Looking forward, holds considerable promise especially ongoing advancements big data. However, challenges standardization, model interpretability, patient privacy protection must be addressed fully unlock potential improving diagnostic precision outcomes.

Language: Английский

Citations

0