Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer DOI Open Access
Min Zhang, Ya Li, Yongxiang Hu

et al.

Translational Cancer Research, Journal Year: 2023, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2023

There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction is essential treatment. This study proposes to explore potential multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker prognostic risk stratification NSCLC patients receiving radiochemotherapy. In this study, 365 histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, Karnofsky Performance Scale (KPS) scores ≥70 were included three medical institutions, 145 cases excluded due surgery, data accuracy, poor image quality, presence other tumors. Finally, 220 study. Efficacy evaluation criteria solid tumors used evaluate efficacy. Complete partial remission indicate radiochemotherapy-sensitive group, disease stability progression radiochemotherapy-resistant group. We all then randomised them into training cohort (154 cases) validation (66 7:3 ratio. Radiomics features extracted gross tumor volume (GTV), GTV-heat, 50 Gy-heat screened. 2D model (DMGTV DM50Gy), (RMGTV), radiomics-dosiomics (RDM), models constructed, predictive performances resistance compared. Subsequently, performance various was compared by receiver operating characteristic (ROC) curves calculating sensitivity specificity. The multi-omics clinical integrated patient stratification. DM50Gy better than RMGTV DMGTV, area under curve (AUC) ROC cohorts 0.764 [95% confidence interval (CI): 0.687-0.841] 0.729 (95% CI: 0.568-0.889). And RDM performed significantly single models, AUC 0.836 0.773-0.899) 0.748 0.617-0.879), respectively. Hemoglobin level T stage independent predictors model. containing further improved both cohorts, 0.844 0.781-0.907) 0.753 0.618-0.887). Grouping according critical value revealed significant progression-free survival (PFS) overall (OS) between high-risk low-risk groups (P<0.05). Compared traditional model, demonstrates superior performance. based on data, radiomics, has effectively Through precise assessment, doctors can understand which may develop treatment optimize plans accordingly.

Language: Английский

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology DOI Creative Commons
Elena Fountzilas, Tillman Pearce, Mehmet A. Baysal

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 31, 2025

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field precision oncology, promising to improve diagnostic approaches therapeutic strategies for patients cancer. By analyzing multi-dimensional, multiomic, spatial pathology, radiomic data, these enable a deeper understanding intricate molecular pathways, aiding in identification critical nodes within tumor's biology optimize treatment selection. applications AI/ML oncology are extensive include generation synthetic e.g., digital twins, order provide necessary information design or expedite conduct clinical trials. Currently, many operational technical challenges exist related data technology, engineering, storage; algorithm development structures; quality quantity pipeline; sharing generalizability; incorporation into current workflow reimbursement models.

Language: Английский

Citations

4
Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients DOI Creative Commons
Xuan Wu, Jinyong Wang, Chao Chen

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims develop and validate a model that integrates deep learning sub-regional radiomics from MRI imaging predict pathological complete (pCR) LARC. We retrospectively enrolled 768 eligible participants three independent hospitals who had received followed by radical surgery. Pretreatment pelvic scans (T2-weighted), were collected annotation feature extraction. K-means approach was used segment the tumor into sub-regions. Radiomics features extracted Pyradiomics 3D ResNet50, respectively. predictive models developed using radiomics, machine algorithm training cohort, then validated external tests. models' performance assessed various metrics, including area under curve (AUC), decision analysis, Kaplan-Meier survival analysis. constructed combined model, named SRADL, which includes signatures, enabling pCR LARC patients. SRADL satisfactory cohort (AUC 0.925 [95% CI 0.894 0.948]), test 1 0.915 0.869 0.949]) 2 0.902 0.846 0.945]). By employing optimal threshold 0.486, predicted group longer compared non-pCR across cohorts. also outperformed other single-modality models. novel showed high accuracy robustness predicting pretreatment images, making it promising tool personalized management

Language: Английский

Citations

0
Predicting the early therapeutic response to hepatic artery infusion chemotherapy in patients with unresectable HCC using a contrast-enhanced computed tomography-based habitat radiomics model: a multi-center retrospective study DOI Creative Commons
Mingsong Wu,

Zenglong Que,

Shujie Lai

et al.

Cellular Oncology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients unresectable hepatocellular carcinoma (HCC). Herein, we investigated potential a contrast-enhanced CT-based habitat radiomics model as novel approach predicting early to HAIC-FOLFOX in HCC. A total 148 HCC who received combined targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor features extracted from subregion based on CECT different phases using k-means clustering. Logistic regression was used construct model. This CECT-based verified by bootstrapping compared clinical variables. Model performance evaluated area under curve (AUC) calibration curve. Three intratumoral habitats high, moderate, low enhancement identified prediction. Patients greater proportion high-enhancement showed better responses. The AUC 0.857 (95% CI: 0.798–0.916), bootstrap-corrected concordance index 0.842 0.785–0.907), resulting predictive value than variable-based model, which had an 0.757 0.679–0.834). is effective, visualized, noninvasive tool treatment could guide management decision-making.

Language: Английский

Citations

0
From images to clinical insights: an educational review on radiomics in lung diseases DOI Open Access

C. Magnin,

David Lauer,

Michael Ammeter

et al.

Breathe, Journal Year: 2025, Volume and Issue: 21(1), P. 230225 - 230225

Published: Jan. 1, 2025

Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents significant advancement imaging, offering powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture wavelet characteristics from medical images that can uncover detailed often subtle information goes beyond visual capabilities radiological examiners. By extracting this information, radiomics provide deep insights into pathophysiology diseases support decision-making as well personalised medicine approaches. In educational review, we step-by-step guide radiomics-based analysis, discussing technical challenges pitfalls, outline potential applications diagnosing, prognosticating evaluating treatment responses respiratory medicine.

Language: Английский

Citations

0
Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer DOI Creative Commons

Yexin Su,

Hongyue Zhao,

Zhehao Lyu

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Preoperative knowledge of the status lymphovascular invasion (LVI) in colorectal cancer (CRC) patients can provide valuable information for choosing appropriate treatment strategies. This study aimed to explore value heterogeneity features derived from habitat analysis 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images predicting LVI. Pretreatment 18F-FDG PET/computed (CT) 177 diagnosed with CRC were retrospectively obtained (training cohort, n=106; validation n=71). Conventional radiomics and habitat-derived tumor extracted PET scans. The output probabilities imaging-based random forest model used generate a score (Radscore) intratumoral (ITHscore). Multivariate logistic regression was determine independent risk factors On this basis, four LVI classification models developed using (a) clinical variables (Clinical model), (b) (ITHscore (c) (Radscore (d) variables, features, (Combined model). area under curve (AUC) decision evaluate performance. Among all PET/CT-reported lymph node status, ITHscore, Radscore retained as predictors related (P<0.05). predictive effect ITHscore (AUC: 0.712) better than that 0.650) Clinical 0.652) cohort. Combined achieved effects usefulness, AUCs training cohorts 0.857 0.798, respectively. A nomogram established, calibration plot well fitted (P>0.05). In addition, results Spearman's rank correlation tests showed there no significant between (R=0.044, P=0.655 cohort; R=0.067, P=0.580 cohort). Our is novel stable quantitative indicator helpful effectively facilitating stratification after integrating features.

Language: Английский

Citations

0
From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients DOI Creative Commons
Yusheng Guo,

Tianxiang Li,

Bingxin Gong

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 12(2)

Published: Nov. 13, 2024

Abstract With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined methodology associating gene expression information from high‐throughput technologies with imaging phenotypes. However, advancements medical imaging, omics technologies, and artificial intelligence, both concept application of have significantly broadened. In this review, history enumerated, related five basic workflows their applications across tumors, role AI radiogenomics, opportunities challenges tumor heterogeneity, immune microenvironment. The positron emission tomography multi‐omics studies also discussed. Finally, faced by clinical transformation, along future trends field

Language: Английский

Citations

3
Sub-regional CT Radiomics for the Prediction of Ki-67 Proliferation Index in Gastrointestinal Stromal Tumors: A Multi-center Study DOI

Wemin Cai,

Kun Guo, Yongxian Chen

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 31(12), P. 4974 - 4984

Published: July 20, 2024

Language: Английский

Citations

1
[Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer]. DOI

Yue Hou,

Tianming Zhang, Hong Wang

et al.

PubMed, Journal Year: 2024, Volume and Issue: 27(8), P. 637 - 644

Published: Aug. 20, 2024

Lung cancer is the main cause of cancer-related deaths, with non-small cell lung (NSCLC) being predominant subtype. At present, immunotherapy represented by immune checkpoint inhibitors (ICIs) programmed death receptor 1 or its ligand has been widely used in clinical diagnosis and treatment patients NSCLC. However, only a few can benefit from it, reliable predictive markers for are lacking. Radiomics tool that uses computer software algorithms to extract large amount quantitative information biomedical images. A number studies have confirmed radiomic model predicts efficacy NSCLC be as new type marker, which expected guide individualized decisions bright application prospect. This article reviews research progress radiomics predicting therapy response NSCLC, identifying pseudo-progression hyperprogression, ICIs-related pneumonia, cachexia risk, combining other genomics. .

Language: Английский

Citations

0
Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches DOI Creative Commons
Meng Qi,

Weiding Zhou,

Ying Yuan

et al.

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

Published: Nov. 11, 2024

To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) further evaluate stratified effect model on disease-free survival (DFS) overall (OS) LSCC patients.

Language: Английский

Citations

0
Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer DOI Open Access
Min Zhang, Ya Li, Yongxiang Hu

et al.

Translational Cancer Research, Journal Year: 2023, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2023

There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction is essential treatment. This study proposes to explore potential multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker prognostic risk stratification NSCLC patients receiving radiochemotherapy. In this study, 365 histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, Karnofsky Performance Scale (KPS) scores ≥70 were included three medical institutions, 145 cases excluded due surgery, data accuracy, poor image quality, presence other tumors. Finally, 220 study. Efficacy evaluation criteria solid tumors used evaluate efficacy. Complete partial remission indicate radiochemotherapy-sensitive group, disease stability progression radiochemotherapy-resistant group. We all then randomised them into training cohort (154 cases) validation (66 7:3 ratio. Radiomics features extracted gross tumor volume (GTV), GTV-heat, 50 Gy-heat screened. 2D model (DMGTV DM50Gy), (RMGTV), radiomics-dosiomics (RDM), models constructed, predictive performances resistance compared. Subsequently, performance various was compared by receiver operating characteristic (ROC) curves calculating sensitivity specificity. The multi-omics clinical integrated patient stratification. DM50Gy better than RMGTV DMGTV, area under curve (AUC) ROC cohorts 0.764 [95% confidence interval (CI): 0.687-0.841] 0.729 (95% CI: 0.568-0.889). And RDM performed significantly single models, AUC 0.836 0.773-0.899) 0.748 0.617-0.879), respectively. Hemoglobin level T stage independent predictors model. containing further improved both cohorts, 0.844 0.781-0.907) 0.753 0.618-0.887). Grouping according critical value revealed significant progression-free survival (PFS) overall (OS) between high-risk low-risk groups (P<0.05). Compared traditional model, demonstrates superior performance. based on data, radiomics, has effectively Through precise assessment, doctors can understand which may develop treatment optimize plans accordingly.

Language: Английский

Citations

0