Prediction of Persistent Tumor Status in Nasopharyngeal Carcinoma Post-Radiotherapy-Related Treatment: A Machine Learning Approach DOI Open Access
Hsien‐Chun Tseng, Chao‐Yu Shen,

Pan‐Fu Kao

et al.

Cancers, Journal Year: 2024, Volume and Issue: 17(1), P. 96 - 96

Published: Dec. 31, 2024

Background/Objectives: The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual presence and early recurrence, associated poorer survival outcomes. To address this, we developed prediction model identify at high risk persistent status prior initiating treatment. Methods: This retrospective study included 104 NPC receiving who had completed 3-year follow-up period; 29 were classified into group 75 disease-free group. Radiomic features extracted from pretreatment positron emission tomography (PET) images used construct by employing machine learning algorithms. model’s diagnostic performance was assessed using area under receiver operating characteristic curve (AUC), whereas SHapley Additive exPlanations (SHAP) analysis conducted determine contribution individual model. Results: AdaBoost algorithm validated through five-fold cross-validation achieved highest AUC 0.934. Its sensitivity, specificity, positive predictive value, negative accuracy 89.66%, 86.67%, 72.22%, 95.59%, 87.5%, respectively. SHAP revealed that feature dependence low metabolic uptake emphasis50 greatest impact on predictions. Furthermore, as exhibited markedly higher overall rates compared those status. Conclusions: In conclusion, proposed efficiently identified radiomic PET images.

Language: Английский

The Evolution of Artificial Intelligence in Nuclear Medicine DOI Creative Commons
Leonor Lopes, Alejandro López-Montes, Yizhou Chen

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration artificial intelligence (AI) is one latest revolutionizing chapters, promising significant advancements in diagnosis, prognosis, segmentation, image quality enhancement, theranostics. Early AI applications nuclear focused on diagnostic accuracy, leveraging machine learning algorithms for disease classification outcome prediction. Advances deep learning, including convolutional more recently transformer-based neural networks, have further enabled precise segmentation as well low-dose imaging, patient-specific dosimetry personalized treatment. Generative AI, driven by large language models diffusion techniques, now allowing process, interpretation, generation complex medical images. Despite these achievements, challenges such data scarcity, heterogeneity, ethical concerns remain barriers to clinical translation. Addressing issues through interdisciplinary collaboration will pave way a broader adoption medicine, potentially enhancing patient care optimizing therapeutic outcomes.

Language: Английский

Citations

1
Integrating 18F-FDG PET/CT radiomics and body composition for enhanced prognostic assessment in patients with esophageal cancer DOI Creative Commons
Yeye Zhou, Jin Zhou, Xiaowei Cai

et al.

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

Published: Nov. 14, 2024

This study aimed to develop a predictive model utilizing radiomics and body composition features derived from 18F-FDG PET/CT scans forecast progression-free survival (PFS) overall (OS) outcomes in patients with esophageal squamous cell carcinoma (ESCC). We analyzed data 91 who underwent baseline imaging. Radiomic extracted PET CT images subsequent scores (Rad-scores) were calculated. Body metrics also quantified, including muscle fat distribution at the L3 level scans. Multiparametric models constructed using Cox regression analysis, their performance was assessed area under time-dependent receiver operating characteristic (ROC) curve (AUC) concordance index (C-index). Multivariate analysis identified Rad-scorePFS (P = 0.003), sarcopenia < 0.001), visceral adipose tissue (VATI) 0.001) as independent predictors of PFS. For OS, Rad-scoreOS 0.002), VATI 0.037), stage 0.042), mass (BMI) 0.008) confirmed prognostic factors. Integration Rad-score clinical variables parameters enhanced accuracy, yielding C-indices 0.810 (95% CI: 0.737–0.884) for PFS 0.806 0.720–0.891) OS. underscored potential combining refine assessment ESCC patients.

Language: Английский

Citations

3
Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods DOI Creative Commons
Mengni Zhang, Shipeng Zhang,

Xudong Ao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 13, 2025

The present study analyzed the impact of age on causes death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy (CRT) using machine learning approaches. A total 2841 (1037 classified as older, ≥ 60 years and 1804 younger, < years) were enrolled. Variations CODs between two groups before after applying inverse probability treatment weighting (IPTW). Additionally, seven different models employed predictive tools to identify key variables assess therapeutic outcomes NPC receiving CRT. younger group exhibited a significantly longer overall survival (OS) than older group, both IPTW adjustment (140 vs. 50 months, P 0.001) (137 53 0.001). After IPTW, was associated worse 5-, 10-, 15-year cumulative incidences terms NPC-related deaths (30, 34, 38% 21, 27, 30%; 0.001), cardiovascular disease (CVD; 4.1, 7.2, 8.8% 0.5, 1.8, 3.0%; other (8.3, 17, 24% 8.7, 12%; However, secondary malignant neoplasms comparable (P = 0.100). random forest (RF) model demonstrated highest concordance index 0.701 among all models. Time-dependent variable importance plots indicated that most influential factor affecting 3-, 10-year survival, followed by metastasis tumor stage. Younger had OS their counterparts. Older higher likelihood dying from non-NPC-related causes, particularly CVDs. RF showed best accuracy, identifying critical influencing

Language: Английский

Citations

0
An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: a multicenter study DOI
Hai Liao, Yang Zhao, Wei Pei

et al.

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

Published: Feb. 10, 2025

Language: Английский

Citations

0
The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients DOI Creative Commons
Baoqiang Ma, Alessia de Biase, Jiapan Guo

et al.

Physics and Imaging in Radiation Oncology, Journal Year: 2025, Volume and Issue: 33, P. 100733 - 100733

Published: Jan. 1, 2025

Deep learning (DL) models can extract prognostic image features from pre-treatment PET/CT scans. The study objective was to explore the potential benefits of incorporating pathologic lymph node (PL) spatial information in addition that primary tumor (PT) DL-based for predicting local control (LC), regional (RC), distant-metastasis-free survival (DMFS), and overall (OS) oropharyngeal cancer (OPC) patients. included 409 OPC patients treated with definitive (chemo)radiotherapy between 2010 2022. Patient data, including scans, manually contoured PT (GTVp) PL (GTVln) structures, clinical variables, endpoints, were collected. Firstly, a method employed segment tumours PET/CT, resulting predicted probability maps (TPMp) (TPMln). Secondly, different combinations CT, PET, manual contours 300 used train outcome prediction each endpoint through 5-fold cross validation. Model performance, assessed by concordance index (C-index), evaluated using test set 100 Including improved C-index results all endpoints except LC. For LC, comparable C-indices (around 0.66) observed trained only those as additional structure. Models combined into single structure achieved highest 0.65 0.80 RC DMFS prediction, respectively. these target structures separate entities 0.70 OS. Incorporating performance RC, DMFS,

Language: Английский

Citations

0
Optimization of the 9th Ajcc/Uicc Tnm Staging System for Nasopharyngeal Carcinoma Based on Age and Pretreatment Ebv DNA: Exploring Prognostic Stratification and Individualized Treatment Strategies for Stage Ii-Iii Patients DOI
Yan Chang,

Guoxiang Lin,

Zhongguo Liang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT DOI Creative Commons

Yujun Geng,

Xinlei Zhang,

Ming Zhang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: May 2, 2025

Objective Early detection and timely surgical intervention are crucial in reducing mortality rates associated with clinically significant prostate cancer (csPCa). Currently, clinical diagnostics primarily depend on magnetic resonance imaging (MRI) nuclear medicine, the potential diagnostic value of abdominal computed tomography (CT) remaining underexplored. This study aims to evaluate effectiveness multi-task deep learning neural networks identifying early-stage using CT scans. Methods In this study, we enrolled 539 patients from Department Radiology (N=461) Nuclear Medicine (N=78). We utilized a network model (MTDL), based 3DUnet architecture, segment analyze collected plain images. The predictive performance was compared radiomics single-task ResNet18. A nomogram then developed approach, incorporating prediction results PSAD, age. different models evaluated receiver operating characteristic (ROC) curve area under (AUC). Results 461 were divided into training test sets at ratio 6:4, while formed validation set. Our MTDL demonstrated AUCs 0.941 (95% confidence interval [CI]: 0.905valceedi 0.912 CI: 0.904valceedi 0.932 0.883valceed training, test, cohorts, respectively. indicates that combining effectively diagnoses csPCa, offering superior models. Additionally, outperformed both accuracy. Conclusion can accurately predict presence scans, for early diagnosis cancer.

Language: Английский

Citations

0
Should end-to-end deep learning replace handcrafted radiomics? DOI Creative Commons
Irène Buvat, Joyita Dutta, Abhinav K. Jha

et al.

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

Citations

0
Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI DOI Open Access
Zonglin Liu, Runqi Meng,

Qiong Ma

et al.

Radiology Imaging Cancer, Journal Year: 2025, Volume and Issue: 7(3)

Published: May 1, 2025

MultiRecNet, a fully automatic multitask deep learning network, accurately predicted disease-free survival in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy, using multimodal MRI.

Language: Английский

Citations

0
Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images DOI Creative Commons
Mingyuan Meng, Bingxin Gu, Michael Fulham

et al.

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: Oct. 14, 2024

Early survival prediction is vital for the clinical management of cancer patients, as tumors can be better controlled with personalized treatment planning. Traditional methods are based on radiomics feature engineering and/or indicators (e.g., staging). Recently, models advances in deep learning techniques have achieved state-of-the-art performance end-to-end by exploiting features derived from medical images. However, existing heavily reliant prognostic information within primary and cannot effectively leverage out-of-tumor characterizing local tumor metastasis adjacent tissue invasion. Also, sub-optimal leveraging multi-modality images they rely empirically designed fusion strategies to integrate information, where pre-defined domain-specific human prior knowledge inherently limited adaptability. Here, we present an Adaptive Multi-modality Segmentation-to-Survival model (AdaMSS) The AdaMSS self-adapt its strategy training data also adapt focus regions capture outside tumors. Extensive experiments two large datasets (1380 patients nine centers) show that our surmounts (C-index: 0.804 0.757), demonstrating potential facilitate

Language: Английский

Citations

1