ReviewCureus2024
The Diagnostic Impact of Contrast-Enhanced Computed Tomography (CECT) in Evaluating Lymph Node Involvement in Colorectal Cancer: A Comprehensive Review.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Dual-layer spectral detector computed tomography of peritumoral fat for preoperative prediction of tumor budding in colorectal adenocarcinoma.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- The Incidence of Contrast-Induced Nephropathy Among Low-Risk Cancer Patients with Preserved Renal Function on Active Treatment Undergoing Contrast-Enhanced Computed Tomography: A Single-Site Experience.Healthcare (Basel, Switzerland) · 2026Article
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) remains a significant global health burden, necessitating accurate staging and treatment planning for optimal patient outcomes. Lymph node involvement is a critical determinant of prognosis in CRC, emphasizing the importance of reliable imaging techniques for its evaluation. Contrast-enhanced computed tomography (CECT) has emerged as a cornerstone in CRC imaging, offering high-resolution anatomical detail and vascular assessment. This comprehensive review synthesizes the existing literature to evaluate the diagnostic impact of CECT in assessing lymph node involvement in CRC. Key findings highlight CECT's high sensitivity and specificity in detecting lymph node metastases, facilitating accurate staging and treatment selection. However, challenges such as limited resolution for small lymph nodes and potential false-positives call for a cautious interpretation. Recommendations for clinical practice suggest the integration of CECT into multidisciplinary treatment algorithms, optimizing imaging protocols and enhancing collaboration between radiologists and clinicians. Future research directions include refining imaging protocols, comparative effectiveness studies with emerging modalities, and prospective validation of CECT's prognostic value. Overall, this review stresses the pivotal role of CECT in CRC management and identifies avenues for further advancements in imaging-guided oncology care.
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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.