CT imaging analysis differentiating papillary renal neoplasm with reverse polarity from papillary renal cell carcinoma: combined with a radiomics model

Purpose To assess the computed tomography (CT) findings of papillary renal neoplasm with reverse polarity (PRNRP) and develop a radiomics-based model to distinguish PRNRPs from papillary renal cell carcinomas (PRCCs). Materials and methods We analyzed 31 PRNRPs and 68 PRCCs using preoperative kidney...

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Bibliographic Details
Published in:Japanese journal of radiology 2024-12, Vol.42 (12), p.1458-1468
Main Authors: Lee, Hyo Jeong, Kim, Taek Min, Cho, Jeong Yeon, Moon, Min Hoan, Moon, Kyung Chul, Kim, Sang Youn
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Algorithms
Attenuation
Carcinoma, Renal Cell - diagnostic imaging
Cell differentiation
Computed tomography
Confidence intervals
Diagnosis, Differential
Diagnostic systems
Female
Heterogeneity
Humans
Imaging
Kidney - diagnostic imaging
Kidney cancer
Kidney Neoplasms - diagnostic imaging
Male
Medical imaging
Medicine
Medicine & Public Health
Middle Aged
Multivariate analysis
Neoplasia
Nuclear Medicine
Original
Original Article
Papillary renal cell carcinoma
Parameters
Performance evaluation
Radiographic Image Interpretation, Computer-Assisted - methods
Radiology
Radiomics
Radiotherapy
Regression models
Retrospective Studies
Statistical analysis
Tomography, X-Ray Computed - methods
Tumors
Citations: Items that this one cites
Online Access:Get full text
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Summary:Purpose To assess the computed tomography (CT) findings of papillary renal neoplasm with reverse polarity (PRNRP) and develop a radiomics-based model to distinguish PRNRPs from papillary renal cell carcinomas (PRCCs). Materials and methods We analyzed 31 PRNRPs and 68 PRCCs using preoperative kidney CT. We evaluated CT features that could discriminate PRNRPs from PRCCs. A radiomics signature was constructed using features selected through a least absolute shrinkage and selection operator algorithm. A radiomics-based model incorporating a radiomics signature and subjective CT parameters using multivariate logistic regression was developed. The diagnostic performance of the CT parameters, radiomics model, and their combination was evaluated using the area under the curve (AUC). Results Most of PRNRPs had a round shape (93.5%), well-defined margin (100%), and persistent enhancement (77.4%). Compared with PRCC, PRNRPs exhibited distinct CT features including small size (16.7 vs. 37.7 mm, P  
ISSN:1867-1071
1867-108X
1867-108X
DOI:10.1007/s11604-024-01631-2