Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma.

Cancers (Basel) · 2024 · PMC11429600 · PMID 39335090

Europe PMC DOI

hedged sentences

0.0001

closest p · 0.0× alpha

0.0600

boldest claim

The sentences

highly significantp < 0.0001actually significant

The model exhibited good discriminatory power with a C-index of 0.731 and a highly significant likelihood ratio test ( p < 0.0001).

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a trend towards significancep = 0.06so close (0.05 < p ≤ 0.1)

The radiomics feature original_shape_Elongation demonstrated a trend towards significance (HR = 0.45, 95% CI: 0.2–1.03, p = 0.06), potentially indicating a protective effect.

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marginally significantp = 0.06so close (0.05 < p ≤ 0.1)

In the radiomics model, wavelet.LHL_glcm_Correlation emerged as a marginally significant predictor (HR = 2.09, 95% CI: 2.59–98.60, p = 0.06), suggesting its potential relevance in survival prediction and underscoring the value of texture-based imaging features in capturing tumor characteristics.

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