Multiparametric radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma DOI Creative Commons
Weiwei Luo, Jinghui Dong, Jiaying Deng

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Abstract Background Surgery can lead to radical cure and long-term survival for individuals with esophageal squamous cell carcinoma (ESCC). Nevertheless, the rates markedly vary among patients. Accurately predicting surgical efficacy remains a pressing issue. This investigation sought examine predictive value of preoperative radiomics prognostic nutritional index ESCC construct comprehensive model estimating postoperative overall (OS) ESCC. Methods research conducted retrospective examination 466 from two medical centers. The data were arbitrarily categorized into training cohort (TC, hospital 1, 246 cases), an internal validation (IVC, 106 external (EVC, 2, 114 cases). Upon demarcation area interest, radiological features extracted. least absolute shrinkage selection operator (LASSO) regression was utilized identify optimal calculate score (RS). After delineation region procured. Subsequently, LASSO employed ascertain RS. independent influencing factors acquired through Cox analyses incorporated RS establish combined nomogram. capability examined utilizing concordance index, time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis. Results In integrating tumor–node–metastasis (TNM) stage forecasting 3-year OS rate, under ROC (AUC) 0.812, 0.748, 0.810 in TC, IVC, EVCs, respectively, thereby demonstrating outstanding significance. superior AUC values TNM prediction which 0.717, 0.612, 0.699, respectively. indexes EVCs 0.780, 0.760, 0.764, curves illustrated nomogram’s remarkable performance clinical application value. Conclusion this investigation, developed by index. predict rate patients could be as tool risk stratification.

Language: Английский

Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer DOI

Maolin Xu,

Hui-Min Yang, Jia Sun

et al.

Academic Radiology, Journal Year: 2023, Volume and Issue: 31(5), P. 1748 - 1761

Published: Dec. 13, 2023

Language: Английский

Citations

5
A radiomics model of contrast-enhanced computed tomography for predicting post-acute pancreatitis diabetes mellitus DOI Open Access
Ran Hu, Hua Yang,

G Zeng

et al.

Quantitative Imaging in Medicine and Surgery, Journal Year: 2024, Volume and Issue: 14(3), P. 2267 - 2279

Published: Feb. 6, 2024

Diabetes mellitus can occur after acute pancreatitis (AP), but the accurate quantitative methods to predict post-acute diabetes (PPDM-A) are lacking. This retrospective study aimed establish a radiomics model based on contrast-enhanced computed tomography (CECT) for predicting PPDM-A.

Language: Английский

Citations

1
Combining Potential Strain Elastography and Radiomics for Diagnosing Breast Lesions in BI-RADS 4: Construction and Validation a Predictive Nomogram DOI
Hailing Zha, Tingting Wu, Manqi Zhang

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 31(8), P. 3106 - 3116

Published: Feb. 20, 2024

Language: Английский

Citations

1
Improving Prediction Accuracy of Residual Axillary Lymph Node Metastases in Node-Positive Triple-Negative Breast Cancer: A Radiomics Analysis of Ultrasound-Guided Clip Locations Using the SHAP Method DOI
Qing Yao, Yu Du, Wei Liu

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

1
Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences DOI Creative Commons

Yaxin Guo,

Jun Liao,

Shunian Li

et al.

Breast Cancer Targets and Therapy, Journal Year: 2024, Volume and Issue: Volume 16, P. 981 - 991

Published: Dec. 1, 2024

Histological grade is an acknowledged prognostic factor for breast cancer, essential determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- peritumoral radiomics models using T2WI DWI MR sequences predicting the histological of cancer.

Language: Английский

Citations

1
Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features DOI Creative Commons
Gang Liang, Suxin Zhang,

Yiquan Zheng

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 5, 2024

Abstract Background To develop a predictive nomogram for breast cancer lympho-vascular invasion (LVI), based on digital tomography (DBT) data obtained from intra- and peri-tumoral regions. Methods 192 patients were enrolled in this retrospective study 2 institutions, which Institution 1 served as the basis training (n = 113) testing 49) sets, while external validation set 30). Tumor regions of interest (ROI) manually-delineated DBT images, ROI was defined mm around intra-tumoral ROI. Radiomics features extracted, logistic regression used to construct intra-, peri-, intra-+peri-tumoral “omics” models. Patient clinical analyzed by both uni- multi-variable analyses identify independent risk factors imaging model, combination most optimal models comprised comprehensive model. The best-performing model out 3 types (“omics”, imaging, comprehensive) identified using receiver operating characteristic (ROC) curve analysis, nomogram. Results LVI, maximum tumor diameter (odds ratio [OR] 1.486, 95% confidence interval [CI] 1.082–2.041, P 0.014), suspicious malignant calcifications (OR 2.898, CI 1.232–6.815, 0.015), axillary lymph node (ALN) metastasis 3.615, 1.642–7.962, < 0.001) Furthermore, accurate predicting LVI occurrence, with areas under (AUCs) 0.889, 0.916, 0.862, for, respectively, training, compared (0.858, 0.849, 0.844) (0.743, 0.759, 0.732). resulting nomogram, incorporating radiomics well diameter, calcifications, ALN metastasis, had great correspondence actual diagnoses calibration curve, high utility decision analysis. Conclusion derived features, highly identifying future occurrence cancer, demonstrating its potential an assistive tool clinicians devise individualized treatment regimes.

Language: Английский

Citations

0
Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer DOI

Qinqin Ma,

Xingru Lu,

Qitian Chen

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 31(12), P. 4743 - 4758

Published: Aug. 5, 2024

Language: Английский

Citations

0
The role of multiparametric MRI in predicting lymphovascular invasion in breast cancer patients DOI

Jinhua Wang,

Siqing Jing,

Zhongxian Yang

et al.

Future Oncology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 10

Published: Sept. 13, 2024

This study aims to investigate the efficacy of multifactorial MRI in diagnosing breast cancer, specifically context predicting lymphovascular invasion (LVI).

Language: Английский

Citations

0
Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features DOI Creative Commons
Chunling Zhang, Peng Zhou, Ruobing Li

et al.

BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Oct. 16, 2024

We aim to develop a predictive model for lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC), using magnetic resonance imaging (MRI)-based radiomics features.

Language: Английский

Citations

0
Multiparametric radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma DOI Creative Commons
Weiwei Luo, Jinghui Dong, Jiaying Deng

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Abstract Background Surgery can lead to radical cure and long-term survival for individuals with esophageal squamous cell carcinoma (ESCC). Nevertheless, the rates markedly vary among patients. Accurately predicting surgical efficacy remains a pressing issue. This investigation sought examine predictive value of preoperative radiomics prognostic nutritional index ESCC construct comprehensive model estimating postoperative overall (OS) ESCC. Methods research conducted retrospective examination 466 from two medical centers. The data were arbitrarily categorized into training cohort (TC, hospital 1, 246 cases), an internal validation (IVC, 106 external (EVC, 2, 114 cases). Upon demarcation area interest, radiological features extracted. least absolute shrinkage selection operator (LASSO) regression was utilized identify optimal calculate score (RS). After delineation region procured. Subsequently, LASSO employed ascertain RS. independent influencing factors acquired through Cox analyses incorporated RS establish combined nomogram. capability examined utilizing concordance index, time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis. Results In integrating tumor–node–metastasis (TNM) stage forecasting 3-year OS rate, under ROC (AUC) 0.812, 0.748, 0.810 in TC, IVC, EVCs, respectively, thereby demonstrating outstanding significance. superior AUC values TNM prediction which 0.717, 0.612, 0.699, respectively. indexes EVCs 0.780, 0.760, 0.764, curves illustrated nomogram’s remarkable performance clinical application value. Conclusion this investigation, developed by index. predict rate patients could be as tool risk stratification.

Language: Английский

Citations

0