AI Article Synopsis
- The study aimed to assess how effective computed tomography (CT) radiomics models are in distinguishing gastrointestinal stromal tumors (GIST) from other mesenchymal tumors through analyzing clinical data from 153 patients.
- Using machine learning techniques like Logistic regression and Random Forest, the researchers identified key radiomics features that could aid in diagnosis, achieving varying performances in training (AUC 0.941 for RF) and validation (AUC 0.746 for RF).
- The Random Forest model outperformed the Logistic regression model and provided significant benefits in classifying patients compared to simpler classification methods, indicating strong potential for clinical application.
Article Abstract
Objective: To analyze the diagnostic value of computed tomography (CT) radiomics models in differentiating gastrointestinal stromal tumors (GIST) and other mesenchymal tumors.
Material And Methods: A retrospective analysis of clinical data from 153 patients with pathologically confirmed gastrointestinal mesenchymal tumors treated in our hospital from July 2019 to March 2024 was conducted, including 107 cases of GIST, 18 cases of leiomyoma, and 28 cases of schwannoma. LASSO regression was used for feature selection. Logistic regression and Random Forest (RF) models were established based on selected features using machine learning algorithms, with the dataset divided into training (107 cases) and validation sets (46 cases) at a 7:3 ratio. The diagnostic performance of the models was evaluated using receiver operating characteristic (ROC) curves.
Results: In the training set, there were significant differences between GIST and non-GIST in terms of enhancement degree, age, maximum diameter, and tumor location distribution (P<0.05). A total of 180 radiomics features were extracted using A.K software. LASSO regression reduced the high-dimensional data to 13 radiomics features. Logistic regression and RF models were established based on these 13 features. The AUC for the Logistic regression model was 0.753 in the training set and 0.582 in the validation set, while the AUC for the RF model was 0.941 in the training set and 0.746 in the validation set. The RF model showed higher diagnostic performance than the Logistic regression model (P<0.05). Decision curve analysis showed that the net benefit of the RF model in differentiating GIST was superior to classifying all patients as either GIST or non-GIST and also superior to the Logistic regression model within a probability threshold range of 20%-90%.
Conclusion: The machine learning models based on radiomics features have good diagnostic value in predicting the pathological classification of GIST and other mesenchymal tumors, with the RF model showing superior diagnostic value compared to the Logistic regression model.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1967/s002449912732 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study.
Sci Rep
January 2025
Department of MRI, Zhongshan City People's Hospital, No. 2, Sunwen East Road, Shiqi District, Zhongshan, 528403, Guangdong, China.
To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features.
View Article and Find Full Text PDFDevelopment and Validation of an AI-Based Multimodal Model for Pathological Staging of Gastric Cancer Using CT and Endoscopic Images.
Acad Radiol
January 2025
Guangxi Medical University, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, Nanning, Guangxi 530021, China (C.Z., D.H., B.W., S.W., Y.S., X.W.); Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China (D.H., X.W.). Electronic address:
Rationale And Objectives: Accurate preoperative pathological staging of gastric cancer is crucial for optimal treatment selection and improved patient outcomes. Traditional imaging methods such as CT and endoscopy have limitations in staging accuracy.
Methods: This retrospective study included 691 gastric cancer patients treated from March 2017 to March 2024.
Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.
Acad Radiol
January 2025
Department of Radiology, Southeast University Zhongda Hospital, No. 87 Dingjiaqiao Road, Gulou District, Nanjing, Jiangsu Province, China (M.Y., J.J.). Electronic address:
Rationale And Objectives: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
Materials And Methods: Data of 115 patients with STTs of extremities and trunk were collected from our hospital as the training set, and data of other 70 patients were collected from another center as the external validation set. Outlined Regions of interest included the intratumor and the peritumor region extending outward by 5 mm, then the corresponding radiomics features were extracted respectively.
An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.
Acad Radiol
January 2025
Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China (B.Z., F.M., X.S., S.L., Q.W.); Department of Urology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong 510080, China (Q.W.). Electronic address:
Rationale And Objectives: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Materials And Methods: A total of 1124 patients with histological results and PSA levels between 4 and 10 ng/mL were enrolled from one public dataset and two local institutions. An nnUNet was trained for prostate masks, and a feature extraction module identified suspicious lesion masks.
Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.
Transl Oncol
January 2025
Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China. Electronic address:
Background And Objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!