Purpose: This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).
Methods: After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.
Results: The radiomic model using features from the peritumoral region of 0-3 mm outperformed that using features from 3-6, 6-9, 9-12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0-3 and 3-6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).
Conclusions: CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393703 | PMC |
| http://dx.doi.org/10.3389/fonc.2022.915835 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer.
Sci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFTo establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected.
View Article and Find Full Text PDFVision transformer-based multimodal fusion network for classification of tumor malignancy on breast ultrasound: A retrospective multicenter study.
Int J Med Inform
January 2025
School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, PR China. Electronic address:
Background: In the context of routine breast cancer diagnosis, the precise discrimination between benign and malignant breast masses holds utmost significance. Notably, few prior investigations have concurrently explored the integration of imaging histology features, deep learning characteristics, and clinical parameters. The primary objective of this retrospective study was to pioneer a multimodal feature fusion model tailored for the prediction of breast tumor malignancy, harnessing the potential of ultrasound images.
View Article and Find Full Text PDFSuperior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.
Radiol Med
January 2025
Medical Science Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience.
Methods: From October 2021 to November 2022, this prospective study enrolled 225 participants with 233 breast cancers before treatment (NCT06104189 at clinicaltrials.gov).
Objective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Want 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!