ArticleLa Radiologia medica2023
Radiomics nomogram for predicting axillary lymph node metastasis-a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01792999 (Koning Breast CT for Breast Imaging in China), which is not on this map. Cited by 7 papers, 1 of them a synthesis 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.
Koning Breast CT for Breast Imaging in China
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Combining ultrasound and core needle histology imaging Radiopathomics model in predicting axillary lymph node metastasis in triple-negative breast cancer.World journal of surgical oncology · 2026Article
- Development of a prognostic model for preoperative stage I-III breast cancer using machine learning with integrated cone-beam breast computed tomography data.La Radiologia medica · 2026Article
- The value of breast ultrafast dynamic contrast-enhanced magnetic resonance imaging in diagnosing axillary lymph node metastasis in mass-type invasive ductal carcinoma of the breast.Quantitative imaging in medicine and surgery · 2025Article
- Development and Preliminary Validation of a Novel Convolutional Neural Network Model for Predicting Treatment Response in Patients with Unresectable Hepatocellular Carcinoma Receiving Hepatic Arterial Infusion Chemotherapy.Journal of imaging informatics in medicine · 2024Article
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
purposeCone-beam breast CT (CBBCT) has an inherent limitation that the axilla cannot be imaged in its entirety. We aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced (CE) CBBCT radiomics features to predict axillary lymph node (ALN) metastasis and complement limited axilla coverage. MATERIAL AND
methodsThis retrospective study included 312 patients with breast cancer from two hospitals who underwent CE-CBBCT examination in a clinical trial (NCT01792999) during 2012-2020. Patients from TCIH comprised training set (n = 176) and validation set (n = 43), and patients from SYSUCC comprised external test set (n = 93). 3D ROIs were delineated manually and radiomics features were extracted by 3D Slicer software. RadScore was calculated and radiomics model was constructed after feature selection. Clinical model was built on independent predictors. Nomogram was developed with independent clinical predictors and RadScore. Diagnostic performance was compared among three models by ROC curve, and decision curve analysis (DCA) was used to evaluate the clinical utility of nomogram.
resultsA total of 139 patients were ALN positive and 173 patients were negative. Twelve radiomics features remained after feature selection. Location and focality were selected as independent predictors for ALN status. The AUC of nomogram in external test set was higher than that of clinical model (0.80 vs. 0.66, p = 0.012). DCA demonstrated that the nomogram had higher overall net benefit than that of clinical model.
conclusionThe nomogram combined CE-CBBCT-based radiomics features and clinical factors could have potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.
Indexed as
Identifiers
What 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.