A multi-perspective information aggregation network for automated T-staging detection of nasopharyngeal carcinoma

Liang, Shujun; Dong, Xiuyu; Yang, Kaifan; Chu, Zhiqin; Tang, Fan; Ye, Feng; Chen, Bei; Guan, Jian

doi:10.1088/1361-6560/aca516

Cited by 4 publications

(2 citation statements)

References 29 publications

(26 reference statements)

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“…We hypothesize that integrating data from multiple sequences in future iterations of our multi-task model could further enhance its GTV contouring performance. In previous study focusing on T-stage prediction, a multi-perspective information aggregation framework demonstrated a slightly higher AUC (0.88 vs 0.85) for automatic T-staging in NPC than our multi-task model (34). However, it is important to note that this framework employed a multi-branch architecture with outputs from three parallel branches integrated through major voting, which may have contributed to its enhanced performance.…”

Section: Discussionmentioning

confidence: 63%

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma

Yang,

Dong,

Tang

et al. 2024

Front. Oncol.

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BackgroundAccurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC.ObjectivesTo bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC.Materials and methodsWe have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD).ResultsOur multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82–0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models.ConclusionsThis study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

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Section: Discussionmentioning

confidence: 63%

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma

Yang,

Dong,

Tang

et al. 2024

Front. Oncol.

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“…Tumor sizes and forms vary, making individual slice-by-slice screening for T-staging time intensive. Consequently, a multi-perspective aggregation network (TSD Net) has been created with ideas from oncological diagnostics that included different diagnosis-oriented knowledge and enabled automatic nasopharyngeal carcinoma T-staging identification [104].…”

Section: Tumor Staging and Gradingmentioning

confidence: 99%

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Zhang,

Xu,

Tang

et al. 2023

J Hematol Oncol

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Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

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Artificial general intelligence for radiation oncology

Liu,

Holmes

et al. 2023

Meta-Radiology

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A multi-perspective information aggregation network for automated T-staging detection of nasopharyngeal carcinoma

Cited by 4 publications

References 29 publications

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Artificial general intelligence for radiation oncology

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