ArticleFrontiers in oncology2020
CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma From Yolk Sac Tumors in Children.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 21 citations in OpenAlex.
- Important parameters should be paid attention in PTMC radiofrequency ablation.Scientific reports · 2023Trial
- CT imaging analysis differentiating papillary renal neoplasm with reverse polarity from papillary renal cell carcinoma: combined with a radiomics model.Japanese journal of radiology · 2024Article
- CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children.BMC medical imaging · 2024Article
- Evaluation of clinical and imaging features for differentiating rhabdomyosarcoma from neuroblastoma in pediatric soft tissue.Frontiers in oncology · 2024Article
- A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma.Gland surgery · 2023Article
- Promoting the application of pediatric radiomics via an integrated medical engineering approach.Cancer innovation · 2023Review
- Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children.BMC pediatrics · 2023Article
- Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children.Scientific reports · 2022Article
- [Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma.Frontiers in oncology · 2021Article
- [Artificial intelligence in image evaluation and diagnosis].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to investigate the role of CT radiomics features combined with a support vector machine (SVM) model in potentially differentiating pelvic rhabdomyosarcoma (RMS) from yolk sac tumors (YSTs) in children.
methodsA total of 94 patients with RMS (n = 49) and YSTs (n = 45) were enrolled. Non-enhanced phase (NP), arterial phase (AP), and venous phase (VP) images were retrieved for analysis. The volumes of interest (VOIs) were constructed by segmenting tumor regions on CT images to extract radiomics features. Datasets were randomly divided into two sets including a training set and a test set. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen out the optimal radiomics features that could distinguish RMS from YSTs, and the features were combined with the SVM algorithm to build the classifier model. In the testing set, the areas under the receiver operating characteristic (ROC) curves (AUCs), accuracy, specificity, and sensitivity of the model were calculated to evaluate its diagnostic performance. The clinical factors (including age, sex, tumor site, tumor volume, AFP level) were collected.
resultsIn total, 1,321 features were extracted from the NP, AP, and VP images. The LASSO regression algorithm was used to screen out 23, 26, and 17 related features, respectively. Subsequently, to prevent model overfitting, the 10 features with optimal correlation coefficients were retained. The SVM classifier achieved good diagnostic performance. The AUCs of the NP, AP, and VP radiomics models were 0.937 (95% CI: 0.862, 0.978), 0.973 (95% CI: 0.913, 0.996), and 0.855 (95% CI: 0.762, 0.922) in the training set, respectively, which were confirmed in the test set by AUCs of 0.700 (95% CI: 0.328, 0.940), 0.800 (95% CI: 0.422, 0.979), and 0.750 (95% CI: 0.373, 0.962), respectively. The difference in sex, tumor volume, and AFP level were statistically significant (P < 0.05).
conclusionsThe CT-based radiomics model can be used to effectively distinguish RMS and YST, and combined with clinical features, which can improve diagnostic accuracy and increase the confidence of radiologists in the diagnosis of pelvic solid tumors in children.
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.