PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer

Radiotherapy and Oncology, Год журнала: 2024, Номер 197, С. 110368 - 110368

Опубликована: Июнь 2, 2024

Язык: Английский

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Artificial intelligence uncertainty quantification in radiotherapy applications − A scoping review

Radiotherapy and Oncology, Год журнала: 2024, Номер 201, С. 110542 - 110542

Опубликована: Сен. 17, 2024

Язык: Английский

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Machine learning in image‐based outcome prediction after radiotherapy: A review

Journal of Applied Clinical Medical Physics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

Abstract The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope ML applications various treatment contexts, focusing on outcomes such patient survival, disease recurrence, and treatment‐induced toxicity. It emphasizes ascending trajectory research efforts prominence survival analysis clinical priority. We analyze use several common medical imaging modalities conjunction data, highlighting advantages complexities inherent this approach. reflects commitment to advancing patient‐centered care, advocating for expanded abdominal pancreatic cancers. While data collection, privacy, standardization, interpretability present significant challenges, leveraging holds remarkable promise elevating precision medicine improving care outcomes.

Язык: Английский

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The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients

Physics and Imaging in Radiation Oncology, Год журнала: 2025, Номер 33, С. 100733 - 100733

Опубликована: Янв. 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,

Язык: Английский

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Oropharyngeal Cancer Detection with Machine Learning for Precision Diagnosis

Learning and analytics in intelligent systems, Год журнала: 2025, Номер unknown, С. 12 - 20

Опубликована: Янв. 1, 2025

Язык: Английский

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Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma

EBioMedicine, Год журнала: 2025, Номер 114, С. 105663 - 105663

Опубликована: Март 23, 2025

Язык: Английский

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Uncertainty-Aware Deep Learning for Segmentation of Primary Tumor and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Center Cohort

Computerized Medical Imaging and Graphics, Год журнала: 2025, Номер unknown, С. 102535 - 102535

Опубликована: Март 1, 2025

Язык: Английский

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Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer

JAMA Network Open, Год журнала: 2025, Номер 8(5), С. e258094 - e258094

Опубликована: Май 1, 2025

Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging both regions for predictions p16+ OPSCC outcomes. To develop validate computed tomography (CT)-based deep learning classifier that integrates PT LN features predict outcomes in identify patients stage I disease who may derive added benefit chemotherapy. In this retrospective prognostic study, radiographic CT scans were analyzed 811 treated definitive radiotherapy or chemoradiotherapy from 3 independent cohorts. One cohort the Cancer Imaging Archive (1998-2013) was used model development validation 2 remaining cohorts (2002-2015) externally test performance. The Swin Transformer architecture applied fuse into multiregion risk score (SwinScore) survival across within subpopulations at various stages. Data analysis performed between February July 2024. Definitive treatment OPSCC. Hazard ratios (HRs), log-rank tests, concordance index (C index), net evaluate associations disease-free (DFS), overall (OS), locoregional failure (LRF). Interaction tests conducted assess whether association chemotherapy outcome significantly differs dichotomized subgroups. total patient comprised (median age, 59.0 years [IQR, 47.4-70.6 years]; 683 men [84.2%]). external set, found be DFS (HR, 3.76 [95% CI, 1.99-7.10]; P < .001), OS 4.80 2.22-10.40]; LRF 4.47 1.43-14.00]; = .01) among all score, integrating information, demonstrated higher C (0.63) compared models focusing solely on (0.61) (0.58). Chemotherapy improved only high scores 0.09 0.02-0.47]; .004) but not those low 0.83 0.32-2.10]; .69). This study describes CT-based recurrence suitable candidates tailoring. tool could optimize modulations granular level.

Язык: Английский

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Uncertainties in outcome modelling in radiation oncology

Physics and Imaging in Radiation Oncology, Год журнала: 2025, Номер unknown, С. 100774 - 100774

Опубликована: Май 1, 2025

Язык: Английский

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Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making

Seminars in Radiation Oncology, Год журнала: 2024, Номер 34(4), С. 379 - 394

Опубликована: Сен. 11, 2024

Язык: Английский

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