Background: The peritumoral region possesses attributes that promote cancer growth and progression. However, the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.
Aim: To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer (LARC).
Methods: This retrospective study included 409 patients with biopsy-confirmed LARC treated with neoadjuvant chemoradiotherapy and surgically. Patients were divided into training ( = 273) and validation ( = 136) sets. Based on intratumoral and peritumoral radiomic features extracted from pretreatment axial high-resolution small-field-of-view T2-weighted images, multivariate Cox models for progression-free survival (PFS) prediction were developed with or without clinicoradiological features and evaluated with Harrell's concordance index (C-index), calibration curve, and decision curve analyses. Risk stratification, Kaplan-Meier analysis, and permutation feature importance analysis were performed.
Results: The comprehensive integrated clinical-radiological-omics model (Model) integrating seven peritumoral, three intratumoral, and four clinicoradiological features achieved the highest C-indices (0.836 and 0.801 in the training and validation sets, respectively). This model showed robust calibration and better clinical net benefits, effectively distinguished high-risk from low-risk patients (PFS: 97.2% 67.6% and 95.4% 64.8% in the training and validation sets, respectively; both < 0.001). Three most influential predictors in the comprehensive Model were, in order, a peritumoral, an intratumoral, and a clinicoradiological feature. Notably, the peritumoral model outperformed the intratumoral model (C-index: 0.754 0.670; = 0.015); peritumoral features significantly enhanced the performance of models based on clinicoradiological or intratumoral features or their combinations.
Conclusion: Peritumoral radiomics holds greater prognostic value than intratumoral radiomics for predicting PFS in LARC. The comprehensive model may serve as a reliable tool for better stratification and management postoperatively.
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http://dx.doi.org/10.3748/wjg.v31.i8.99036 | DOI Listing |
World J Gastroenterol
February 2025
Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong Province, China.
Background: The peritumoral region possesses attributes that promote cancer growth and progression. However, the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.
Aim: To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer (LARC).
Int J Surg
January 2025
Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen (Zhongshan) University.
Background: Tumour-infiltrating lymphocytes (TILs) are strongly correlated with the prognosis and immunotherapy response in bladder cancer. The TIL status is typically assessed through microscopy as part of tissue pathology. Here, the authors developed Rad-TIL model, a novel radiomics model, to predict TIL status in patients with bladder cancer.
View Article and Find Full Text PDFInsights Imaging
March 2025
Department of Ultrasound, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Objectives: Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively.
Methods: Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals.
J Immunother Cancer
March 2025
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
Background: Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.
Methods: In this retrospective study, data from patients with resectable LUAD (stage I-III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59).
Front Oncol
February 2025
Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.
Background: Microvascular invasion (MVI) is a key prognostic factor in solitary hepatocellular carcinoma (HCC), significantly affecting treatment decisions and outcomes. Early prediction of MVI is crucial for enhancing clinical decision-making.
Objectives: This study aimed to develop and evaluate four predictive models for MVI: one based on clinical indicators, one on MRI assessments, one using radiomics, and a combined model integrating all data across multiple medical centers.
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