Background: Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images.
Methods: A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities.
Results: Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients.
Conclusion: The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.
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http://dx.doi.org/10.1186/s12885-022-09584-3 | DOI Listing |
Abdom Radiol (NY)
January 2025
Department of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Background: The prognostic prediction of pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aimed to develop a radiomics model to predict Ki-67 expression status in PDAC patients using radiomics features from dual-phase enhanced CT, and integrated clinical characteristics to create a radiomics-clinical nomogram for prognostic prediction.
Methods: In this retrospective study, data were collected from 124 PDAC patients treated surgically at a single center, from January 2017 to March 2023.
Objectives: Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.
Methods: This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness.
Aim: Many combinations of inflammation-based markers have been reported their prognostic ability. The prognostic value of albumin-to-gama-glutamyltransferase ratio (AGR), an inflammation-related index, has been identified for several cancers. However, the predictive value of AGR for high-grade glioma patients remains unclear.
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January 2025
Department of Neonatology, The First Affiliated Hospital of Zheng Zhou University, Zhengzhou, China.
Objective: To explore the risk factors for the reactivate of retinopathy of prematurity (ROP) after intravitreal injection of anti-vascular endothelial growth factor (VEGF) and to construct a nomogram model to predict the risk of ROP reactivate.
Methods: A retrospective analysis was conducted on 185 ROP children who underwent anti-VEGF treatment at the First Affiliated Hospital of Zhengzhou University from January 2017 to October 2023. They were randomly divided into a training set (129 cases) and a validation set (56 cases) at a ratio of 7:3.
Front Surg
January 2025
Department of Cardio-Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Objective: This study aimed to explore the risk factors of hypokalemia after radical resection of esophageal cancer (EC) and establish a nomogram risk prediction model to evaluate hypokalemia risk after esophagectomy. Thus, this study provides a reference for the clinical development of intervention measures.
Methods: Clinical data of EC patients who underwent radical surgery from January 2020 to November 2022 in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected.
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