ArticleAbdominal radiology (New York)2023
Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.Frontiers in oncology · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a magnetic resonance imaging radiomics model to predict tumor deposits (TDs) and prognosis in stage T3 lymph node positive (T3N+) rectal cancer (RC).
methodsThis retrospective study included 163 patients with pathologically confirmed T3N + RC from December 2013 to December 2015. The patients were divided into two groups for training and testing. Extracting radiomic features from MR images and selecting features using principal component analysis (PCA), then radiomic scores (rad-scores) were obtained by logistic regression analysis. Finally, a combined TDs prediction model containing rad-scores and clinical features was developed. A receiver operating characteristic (ROC) curve was used to assess the prediction performance. The overall survival (OS) rate in patients with high-risk and low-risk TDs predicted by rad-scores was validated by Kaplan-Meier survival curves.
resultsOf the 163 patients included, histological TDs was diagnosed in 45 patients. The area under the curve (AUC) of the final model was 0.833 (training) and 0.844 (testing). The patients with rad-scores predicted high-risk were associated with OS. In addition, postoperative adjuvant therapy improved the OS of the high-risk TDs group (P < 0.05).
conclusionMRI-based radiomics modeling helps in the preoperative prediction of patients with TDs+ in T3N + RC and provides risk stratification for neoadjuvant therapy. In addition, the rad-scores of TDs could suggest different survival benefits of postoperative adjuvant therapy for T3N + RC patients and guide clinical treatment.
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