A radiomics-based nomogram for differentiating minimally invasive adenocarcinoma from stage I invasive non-mucinous adenocarcinoma (CTR < 0.5, GGO-dominant).
Aug 2026· Updates in Surgery· 0 citations· 23 references
Medicine
TL;DR
The radiomics-based nomogram model demonstrates excellent predictive performance for differentiating MIA from stage I INMA in GGO-dominant lesions with CTR < 0.5, and provides valuable clinical support for personalized and precise diagnosis and treatment, thereby facilitating optimal patient management.
The proposed multimodal predictive triple-classification model, integrating conventional, follow-up, ITR, and PTR, may act as a non-invasive tool for preoperative evaluation of GGNs invasiveness.
Y. Lv, Jing Ye, Juan Chen· British Journal of Radiology· 0 citations
Background Differentiating the immunohistochemical and histopathological features of ductal carcinoma in situ (DCIS) lesions is critical to potentially enable future tailored treatment options and de-escalation strategies. Purpose To investigate the potential of magnetic resonance imaging (MRI) radiomics for the non-in...
A. A. Tan, Amir Bakhshandehpour, Ümran Esen et al.· Acta Radiologica· 0 citations
RATIONALE AND OBJECTIVES
To investigate the ability of a radiomics model of T2-weighted imaging (T2WI) tumor and peritumoral regions for predicting noninvasively tumor budding grading in rectal patients.
MATERIALS AND METHODS
This retrospective study included 478 patients diagnosed with rectal cancer from three diffe...
To develop and validate an interpretable machine learning (ML) model based on CT radiomics to predict the 2-year disease-free survival (DFS) in muscle invasive bladder cancer (MIBC) patients with multicenter validation.
A total of 377 patients with MIBC who underwent radical cystectomy were included from f...
The clinical–radiomics nomogram shows promising potential for differentiating lymphoma from CUP in poorly differentiated cervical lymph node malignancies and warrants further validation in larger, multi-center cohorts before clinical implementation.
Background Spread through air spaces (STAS) is an important invasive pattern associated with aggressive behaviour and prognosis in lung adenocarcinoma. However, whether nodule subtype influences the performance of computed tomography (CT)-based radiomics for preoperative STAS prediction remains unclear. This study aime...
Mei-Rong Li, Ya-Chao Ruan, Yun-Fei Sheng et al.· Quantitative Imaging in Medi...· 0 citations
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