Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study DOI Creative Commons

Xiaojiang Zhao,

Yuhang Wang, Mengli Xue

et al.

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Dec. 18, 2024

Abstract Objective To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of in stage I lung adenocarcinoma patients and to evaluate its potential prognosis stratification guiding personalized treatment. Methods The most recent chest thin-slice scans postoperative hematoxylin eosin-stained pathology sections diagnosed with LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training algorithm three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic extracted from tumor peritumoral areas, extensions 2 mm, 4 6 8 respectively, deep learning image through network. Subsequently, RAITS constructed. performance then evaluated both train validation cohorts. Results demonstrated superior AUC, sensitivity, specificity external cohorts, outperforming traditional unimodal models. In cohort, AUC 0.78 (95% CI, 0.69–0.88) showed higher net benefits across threshold ranges. exhibited strong discriminative ability risk stratification, p < 0.01 cohort = 0.02 consistent actual TLSs, where TLS-positive had significantly recurrence-free survival (RFS) compared TLS-negative ( 0.04 cohort). Conclusion As model features, excellent identifying holds value clinical decision-making.

Language: Английский

Radiomic Fingerprinting of the Peritumoral Edema in Brain Tumors DOI Open Access
Ghasem Azemi, Antonio Di Ieva

Cancers, Journal Year: 2025, Volume and Issue: 17(3), P. 478 - 478

Published: Feb. 1, 2025

Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role tumor behavior and progression. While most studies focus on radiomic features core, this work investigates whether edema exhibits distinct fingerprints specific to glioma (GLI), meningioma (MEN), metastasis (MET). By analyzing these patterns, we aim deepen our understanding microenvironment’s development Methods: Radiomic were extracted from regions T1-weighted (T1), post-gadolinium (T1-c), T2-weighted (T2), T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those then conducted: differentiating between Low-Grade Glioma (LGG) High-Grade (HGG), distinguishing GLI MET MEN, examining all four types, i.e., LGG, HGG, MET, observe how tumor-specific signatures manifest edema. Model performance was assessed balanced accuracy derived 10-fold cross-validation. Results: The types more T1-c images compared other modalities. best models, utilizing images, achieved accuracies 0.86, 0.81, 0.76 for LGG-HGG, GLI-MET-MEN, LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that as characterized by MRIs, contains type, providing non-invasive approach tumor-brain interactions. results hold potential predicting recurrence, progression pseudo-progression, assessing treatment-induced changes, gliomas.

Language: Английский

Citations

0
Radiomics in head and neck squamous cell carcinoma – a leap towards precision oncology DOI Creative Commons
Pranjal Rai, Abhishek Mahajan

Journal for ImmunoTherapy of Cancer, Journal Year: 2025, Volume and Issue: 13(4), P. e011692 - e011692

Published: April 1, 2025

Immunotherapy has revolutionized head and neck squamous cell carcinoma (HNSCC) treatment, with neoadjuvant chemoimmunotherapy showing promising pathological complete response rates (36–42%). Lin et al introduce a radiomics-clinical nomogram using MRI-derived intratumoral peritumoral features to predict pCR, addressing critical clinical gap. Their model, emphasizing the region (within 3 mm), achieved high predictive accuracy area under curve (AUC) >0.8. While multicenter design enhances generalizability, standardizing imaging protocols remains challenge. Integrating radiomics Neck Imaging Reporting Data System could refine post-treatment assessment. This study advances precision oncology in HNSCC, offering non-invasive tool for personalized treatment strategies. Future directions include artificial intelligence-driven radiogenomics enhance prediction patient selection.

Language: Английский

Citations

0
Machine Learning and Computed Tomography Radiomics to Predict Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced Non-Small-Cell Lung Cancer: A Pilot Study DOI Open Access
Ian Janzen, Cheryl Ho, Barbara Melosky

et al.

Cancers, Journal Year: 2024, Volume and Issue: 17(1), P. 58 - 58

Published: Dec. 28, 2024

Background/Objectives: Pembrolizumab monotherapy is approved in Canada for first-line treatment of advanced NSCLC with PD-L1 ≥ 50% and no EGFR/ALK aberrations. However, approximately 55% these patients do not respond to pembrolizumab, underscoring the need early intervention non-responders optimize strategies. Distinguishing sub-cohort prior a real-world dilemma. Methods: In this retrospective study, we analyzed two patient cohorts treated pembrolizumab (training set: n = 97; test 17). The response was assessed using baseline follow-up CT scans via RECIST 1.1 criteria. Results: A logistic regression model, incorporating pre-treatment radiomic features lung tumors clinical variables, achieved high predictive accuracy (AUC: 0.85 training; 0.81 testing, 95% CI: 0.63–0.99). Notably, from peritumoral region were found be independent predictors, complementing standard evaluations other characteristics. Conclusions: This pragmatic model offers valuable tool guide decisions expression has potential advance personalized oncology improve timely disease management.

Language: Английский

Citations

1
Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study DOI
Miaomiao Lin, Kai Li,

Yanni Zou

et al.

Clinical & Translational Oncology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 5, 2024

Language: Английский

Citations

0
Neoadjuvant immunotherapy for non-small cell lung cancer: Opportunities and challenges DOI Creative Commons

Junjie Hu,

Jing Zhang,

Shiyue Wan

et al.

Chinese Medical Journal - Pulmonary and Critical Care Medicine, Journal Year: 2024, Volume and Issue: 2(4), P. 224 - 239

Published: Dec. 1, 2024

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for resectable non-small cell lung cancer. Numerous trials explored use of ICIs, either as monotherapy or in combination with other therapies, neoadjuvant setting stage I-III Most demonstrated immunotherapy to be safe and remarkable efficacy, a high pathological response rate significantly improved event-free survival. This review summarizes findings Phase clinical investigating various regimens, including ICI monotherapy, therapy combined chemotherapy, plus anti-angiogenic therapy, dual radiotherapy chemoradiotherapy. We discuss benefits outcomes associated each approach. Despite results being promising, several unresolved issues remain, identification reliable biomarkers, appropriate duration optimal regimen tumors programmed death ligand 1 (PD-L1) expression, false-negative complete rate, role digital pathology assessing treatment. Resistance immunotherapy, particular, remains significant barrier effective ICIs. Given critical influence tumor microenvironment (TME) on treatment, we examine characteristics TME both responsive resistant well dynamic changes that occur immunotherapy. also summarize mechanisms underlying T responses following provide perspective strategies enhance understanding heterogeneity, therapy-driven remodeling, overcoming resistance therapy. Finally, propose future directions advancements personalized

Language: Английский

Citations

0
Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study DOI Creative Commons

Xiaojiang Zhao,

Yuhang Wang, Mengli Xue

et al.

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Dec. 18, 2024

Abstract Objective To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of in stage I lung adenocarcinoma patients and to evaluate its potential prognosis stratification guiding personalized treatment. Methods The most recent chest thin-slice scans postoperative hematoxylin eosin-stained pathology sections diagnosed with LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training algorithm three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic extracted from tumor peritumoral areas, extensions 2 mm, 4 6 8 respectively, deep learning image through network. Subsequently, RAITS constructed. performance then evaluated both train validation cohorts. Results demonstrated superior AUC, sensitivity, specificity external cohorts, outperforming traditional unimodal models. In cohort, AUC 0.78 (95% CI, 0.69–0.88) showed higher net benefits across threshold ranges. exhibited strong discriminative ability risk stratification, p < 0.01 cohort = 0.02 consistent actual TLSs, where TLS-positive had significantly recurrence-free survival (RFS) compared TLS-negative ( 0.04 cohort). Conclusion As model features, excellent identifying holds value clinical decision-making.

Language: Английский

Citations

0