ArticleRevista da Associacao Medica Brasileira (1992)2025
Multisequence combined magnetic resonance imaging radiomics model to noninvasively predict nuclear grade of clear cell renal cell carcinoma: interpretable model development.
Article in Revista da Associacao Medica Brasileira (1992), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe nuclear grade of clear cell renal cell carcinoma directly relates to prognosis and is usually determined through invasive methods like biopsy or surgery. This study aimed to predict the nuclear grade of clear cell renal cell carcinoma using a noninvasive method: multisequence magnetic resonance imaging-based radiomics analysis.
methodsA total of 42 clear cell renal cell carcinomas (29 low grade, 13 high grade) were included in the study. T2, fat-suppressed T2, noncontrast T1, corticomedullary phase, nephrographic phase, excretory phase, and apparent diffusion coefficient sequences of patients were used for radiomics analysis. Inter-observer agreement was assessed for these sequences, and following reproducibility analysis and feature selection, three new groups were formed: noncontrast enhancement, contrast enhancement, and combined groups, with different combinations of features extracted from these sequences. As a result, seven different sequences and three different groups constituted 10 classification groups. An extreme gradient boosting model was used for classification, employing 10-fold cross-validation.
resultsRadiomics features from corticomedullary phase and nephrographic phase sequences showed excellent inter-observer agreement, with Pearson correlation coefficient values of 0.88 for corticomedullary phase and 0.90 for nephrographic phase. The study included 42 clear cell renal cell carcinomas with a mean age of 60.8 years. Individually, the corticomedullary phase sequence achieved the highest area under the curve and accuracy values (0.88 and 0.85), followed by the apparent diffusion coefficient sequence (0.87 and 0.79). In the combined sequence group, the contrast enhancement group showed the highest area under the curve and accuracy (0.93 and 0.87), ranking highest across the entire study.
conclusionMultisequence magnetic resonance imaging radiomics has great potential to predict the nuclear grade of clear cell renal cell carcinoma and guide the treatment plan noninvasively.
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.