Evaluating the Effectiveness of MRI Versus CT in Identifying Retroperitoneal Lymph Node Metastasis in Testicular Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Testicular cancer, while rare, is a common malignancy among males aged 15-45 years and often spreads to retroperitoneal lymph nodes. This systematic review and meta-analysis compares the diagnostic accuracy of magnetic resonance imaging (MRI) and computed tomography (CT) in detecting retroperitoneal lymph node metastasis. A comprehensive search was conducted up to January 25, 2024, using PubMed, MEDLINE, Web of Science, and Google Scholar. Studies comparing MRI and CT for detecting retroperitoneal lymph node involvement in adult males with testicular neoplasms were included. Data extraction covered study design, sample size, demographics, imaging techniques, and diagnostic outcomes. The Newcastle-Ottawa Scale was used to assess the risk of bias, and a random-effects model was applied for the meta-analysis. A total of 618 articles were identified, with four meeting the inclusion criteria. The studies reported high sensitivity for both MRI and CT, with MRI sensitivities ranging from 97% to 100% and CT sensitivities from 96% to 100%. Specificity findings were variable, with some studies suggesting similar or slightly higher values for MRI. Meta-analysis of three studies revealed no significant difference between MRI and CT in detecting retroperitoneal lymph node metastasis, with an odds ratio of 1.00 (95% CI: 0.54 to 1.86) and minimal heterogeneity (I² = 0%). These findings suggest both MRI and CT demonstrate comparable diagnostic accuracy in detecting retroperitoneal lymph node metastasis in testicular cancer. While MRI avoids ionizing radiation, it requires expert interpretation and is more costly, limiting its accessibility in some settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".