Trial reportEuropean radiology2022
CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.
Trial report in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled it
- Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy.BMC gastroenterology · 2023Trial
- Value of [European journal of nuclear medicine and molecular imaging · 2026Article
- CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- CT-Based Radiomics forCancers · 2025Article
- Predicting pathogenic DNA damage repair gene mutations in prostate cancer patients: a multi-center magnetic resonance imaging radiomics study.Quantitative imaging in medicine and surgery · 2025Article
- Radiomics Models to Predict Tumor Response and Pneumonitis in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy.Journal of clinical medicine · 2025Article
- Advances in the mechanisms, imaging characteristics and management strategies for immune checkpoint inhibitor-related pneumonitis.Frontiers in immunology · 2025Review
- Prognostic nomogram combiningScientific reports · 2024Article
- Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning.BMC cancer · 2024Article
- A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2024Article
- Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach.Frontiers in oncology · 2024Article
- A CT-Based Lung Radiomics Nomogram for Classifying the Severity of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2024Article
- Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.Frontiers in physiology · 2023Article
- Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis.Frontiers in immunology · 2023Review
- A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.Therapeutic advances in respiratory diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectivesThe goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. MATERIALS AND
methodsThis retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.
resultsThe initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846).
conclusionsRadiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. KEY POINTS: The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
Indexed as
Identifiers
34564744What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.