Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

Autor: Zahergivar A; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Yazdian Anari P; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Mendhiratta N; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Lay N; Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Singh S; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Dehghani Firouzabadi F; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Chaurasia A; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Golagha M; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Homayounieh F; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Gautam R; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Harmon S; Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Turkbey E; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Merino M; Pathology Department, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Jones EC; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA., Ball MW; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Turkbey B; Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Linehan WM; Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA., Malayeri AA; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.
Jazyk: angličtina
Zdroj: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2024 Sep; Vol. 60 (3), pp. 1076-1081. Date of Electronic Publication: 2024 Feb 01.
DOI: 10.1002/jmri.29222
Abstrakt: Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC).
Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment.
Study Type: Retrospective analysis of a prospectively maintained cohort.
Population: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023.
Field Strength and Sequences: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences.
Assessment: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures.
Statistical Tests: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported.
Results: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported.
Data Conclusion: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment.
Level of Evidence: 1 TECHNICAL EFFICACY: Stage 2.
(Published 2024. This article is a U.S. Government work and is in the public domain in the USA.)
Databáze: MEDLINE
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