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

Role of Radiomics-based Multiomics Panel in the Microenvironment and Prognosis of Hepatocellular Carcinoma DOI

Ziqian Wu,

Siyu Ouyang,

Jidong Gao

et al.

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

Published: Jan. 1, 2025

Language: Английский

Citations

0
Nomogram for Predicting Survival Post-Immune Therapy in Cholangiocarcinoma Based on Inflammatory Biomarkers DOI Creative Commons
Jianan Jin, Haibo Mou, Yibin Zhou

et al.

Cancer Control, Journal Year: 2024, Volume and Issue: 31

Published: Jan. 1, 2024

Background Immune therapy, especially involving PD-1/PD-L1 inhibitors, has shown promise as a therapeutic option for cholangiocarcinoma. However, limited studies have evaluated survival outcomes in cholangiocarcinoma patients treated with immune therapy. This study aims to develop predictive model evaluate the benefits of therapy Methods retrospective analysis included 120 from Shulan (Hangzhou) Hospital. Univariate and multivariate Cox regression analyses were conducted identify factors associated following A was constructed validated using calibration curves (CC), decision curve (DCA), concordance index (C-index), receiver operating characteristic (ROC) curves. Results identified several potential predictors post-immune cholangiocarcinoma: treatment cycle (<6 vs ≥ 6 months, 95% CI: 0.119-0.586, P = 0.001), neutrophil-to-lymphocyte ratio (NLR <3.08 3.08, 1.864-9.624, carcinoembryonic antigen (CEA <4.13 4.13, 1.175-5.321, 0.017), presence bone metastasis (95% 1.306-6.848, 0.010). The nomogram achieved good accuracy C-index 0.811. CC indicated strong between predicted observed outcomes. Multi-timepoint ROC at 1, 2, 3 years model’s performance (1-year AUC: 0.906, 2-year 0.832, 3-year 0.822). multi-timepoint DCA also demonstrated higher net benefit compared extreme Conclusion model, incorporating key risk demonstrates robust outcomes, offering improved clinical decision-making.

Language: Английский

Citations

2
Unmasking the silent killer: The hidden aggressiveness of signet-ring cell carcinoma in gallbladder cancer DOI Open Access

Zhimeng Cheng,

Zilin Jia,

Xiaoling Li

et al.

BioScience Trends, Journal Year: 2024, Volume and Issue: 18(4), P. 379 - 387

Published: Aug. 24, 2024

The prognostic significance of the signet-ring cell component in gallbladder carcinoma (GBC) has not been systematically evaluated. aim this study was to assess similarities and differences between (GBSRCA) adenocarcinoma (GBAC) terms clinicopathological features long-term survival. Using Surveillance, Epidemiology, End Results (SEER) database, we analyzed 6,612 patients diagnosed with cancer 2000 2021. cohort included 147 GBSRCA 6,465 GBAC. Patients were significantly younger, 33.3% being age 60 or younger compared 23.9% GBAC (p = 0.009). There a higher proportion females group (77.6%) (70.1%, p 0.049). associated more advanced tumor stage (T3-T4: 56.5% vs. 44.4%, P 0.004), rates lymph node metastasis (43.5% 28.0%, < 0.001), poorer differentiation status (poorly undifferentiated: 80.3% 29.7%, 0.001). Survival analysis revealed that had worse overall survival (OS) cancer-specific (CSS) an independent factor for OS (P 0.001) entire cohort, while T N factors CSS GBSRCA. Even after propensity score matching, still prognosis.

Language: Английский

Citations

0
Unveiling the unexplored secret: Aggressive behavior and poor survival in intrahepatic mucinous adenocarcinoma compared to conventional adenocarcinoma DOI Open Access

Wenhui Wang,

Hongjun Lin, Qiang Lu

et al.

BioScience Trends, Journal Year: 2024, Volume and Issue: 18(4), P. 370 - 378

Published: Aug. 28, 2024

Intrahepatic bile duct mucinous adenocarcinoma (IHBDMAC) is a rare pathological subtype of intrahepatic cholangiocarcinoma (IHCC), and its tumor biological features survival outcomes have rarely been explored, especially when compared to the most common subtype, (IHBDAC). Therefore, aim this study was explore clinical IHBDAC IHBDMAC using Surveillance, Epidemiology, End Results (SEER) database from 2000 2021. A total 1,126 patients were included, with 1,083 diagnosed 43 IHBDMAC. Patients presented more advanced T stage (55.8% vs. 36.9%, P = 0.012) higher rate lymph node metastasis (37.2% 24.9%, 0.070). Cox regression identified stage, metastasis, distant as poor predictors, while chemotherapy surgery protective factors. Survival analyses revealed significantly worse overall (OS) cancer-specific (CSS) for (P < 0.05). Even after matching, still had prognosis than those IHBDAC. These findings highlight aggressive nature need tailored therapeutic strategies. Future research should focus on prospective studies molecular insights develop targeted treatments

Language: Английский

Citations

0
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