ArticleWorld journal of radiology2026
Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma.
Article in World journal of radiology, 2026. 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSoft-tissue sarcomas (STS) are heterogeneous mesenchymal malignancies for which surgery remains the mainstay of curative treatment, yet recurrence and mortality rates remain substantial. Nutritional status and body composition measured
aimTo identify prognostic value of nutrition-associated factors for patients with STS treated with excision and to construct a predictive model for nutritional assessment by traditional survival analysis and a random forest machine learning method.
methodsWe retrospectively included 638 patients who were diagnosed with STS and underwent surgical excision from January 2009 to June 2018. Nutrition-associated indicators from peripheral blood tests and routine computed tomography were collected. The primary outcome was overall survival (OS). The secondary outcomes were progression-free survival and length of postoperative hospital stay. The random survival forest (RSF) analysis selected important variables for re progression-free survival and OS, and the random forest analysis selected important variables for length of hospitalization. Nomograms were constructed by the prognostic features to predict survival probabilities.
resultsThe RSF analysis identified stage, hospital duration, subtype, body mass index, and tumour size as important variables for OS. The RSF-based nomogram on nutritional indexes for various clinical outcomes showed consistent calibration capacities on calibration plots and great discriminative abilities on the C-index and Brier score.
conclusionOur study implicated the prognostic value of multiple nutritional assessment indexes for prediction of clinical outcomes in STS, and patients' nutrition status need long-term surveillance and management.
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.