Version 1
: Received: 23 September 2024 / Approved: 24 September 2024 / Online: 25 September 2024 (04:21:39 CEST)
How to cite:
Al Mopti, A. N. M.; Alqahtani, A. M.; Alshehri, A. H. D.; Li, C.; Nabi, G. Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors. Preprints2024, 2024091945. https://doi.org/10.20944/preprints202409.1945.v1
Al Mopti, A. N. M.; Alqahtani, A. M.; Alshehri, A. H. D.; Li, C.; Nabi, G. Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors. Preprints 2024, 2024091945. https://doi.org/10.20944/preprints202409.1945.v1
Al Mopti, A. N. M.; Alqahtani, A. M.; Alshehri, A. H. D.; Li, C.; Nabi, G. Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors. Preprints2024, 2024091945. https://doi.org/10.20944/preprints202409.1945.v1
APA Style
Al Mopti, A. N. M., Alqahtani, A. M., Alshehri, A. H. D., Li, C., & Nabi, G. (2024). Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors. Preprints. https://doi.org/10.20944/preprints202409.1945.v1
Chicago/Turabian Style
Al Mopti, A. N. M., Chunhui Li and Ghulam Nabi. 2024 "Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors" Preprints. https://doi.org/10.20944/preprints202409.1945.v1
Abstract
Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC.
Methods: The study retrospectively analysed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, ra-diomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score.
Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707-0.861) compared to the radiomics (0.759, 95% CI: 0.678-0.840) and clinical (0.653, 95% CI: 0.547-0.759) models. Time-dependent AUC analysis revealed the radiomics model's particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors.
Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumour microenvironment, potentially capturing early signs of tumour invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
