Accurate tumor segmentation and treatment outcome prediction with DeepTOP

Li, Lanlan; Xu, Bin; Zhuang, Zhuokai; Li, Juan; Hu, Yihuang; Hui, Yang; Wang, Xiaolin; Lin, Jie; Zhou, Ruwen; Chen, Weiwei; Ran, Dongzhi; Huang, Meijin; Wang, Dabiao; Luo, Yanxin; Yu, Huichuan

doi:10.1016/j.radonc.2023.109550

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…The authors concluded that automated segmentation significantly reduced the amount of time required for TTB quantification while not producing significant differences in the SUVmean. With a focus on patient outcome, a study by Li et al [ 47 ] (MRI in colorectal cancer) demonstrated the capability of an automated segmentation tool (DeepTOP) to accurately segment tumours and predict a pathologically complete response to chemo/radiotherapy. However, large-scale clinical investigations are needed to better define the clinical relevance of semi-automated methods, especially when assisted by AI-based algorithms.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer

Bianconi,

Salis,

Fravolini

et al. 2023

Sensors

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Background. Head and neck cancer (HNC) is the seventh most common neoplastic disorder at the global level. Contouring HNC lesions on [18F] Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans plays a fundamental role for diagnosis, risk assessment, radiotherapy planning and post-treatment evaluation. However, manual contouring is a lengthy and tedious procedure which requires significant effort from the clinician. Methods. We evaluated the performance of six hand-crafted, training-free methods (four threshold-based, two algorithm-based) for the semi-automated delineation of HNC lesions on FDG PET/CT. This study was carried out on a single-centre population of n=103 subjects, and the standard of reference was manual segmentation generated by nuclear medicine specialists. Figures of merit were the Sørensen–Dice coefficient (DSC) and relative volume difference (RVD). Results. Median DSC ranged between 0.595 and 0.792, median RVD between −22.0% and 87.4%. Click and draw and Nestle’s methods achieved the best segmentation accuracy (median DSC, respectively, 0.792 ± 0.178 and 0.762 ± 0.107; median RVD, respectively, −21.6% ± 1270.8% and −32.7% ± 40.0%) and outperformed the other methods by a significant margin. Nestle’s method also resulted in a lower dispersion of the data, hence showing stronger inter-patient stability. The accuracy of the two best methods was in agreement with the most recent state-of-the art results. Conclusions. Semi-automated PET delineation methods show potential to assist clinicians in the segmentation of HNC lesions on FDG PET/CT images, although manual refinement may sometimes be needed to obtain clinically acceptable ROIs.

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Section: Discussionmentioning

confidence: 99%

Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer

Bianconi,

Salis,

Fravolini

et al. 2023

Sensors

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“…The auto-segmentation function of CNNs is a stability tool for VMAT and IMRT treatment plan design, and it may have further potential in adaptive radiotherapy, which requires repeat CT scans and CTV delineation before each treatment fraction ( 28 ). Moreover, with the advancement of MR-Linac, automatic segmentation based on magnetic resonance images has been applied to adaptive radiotherapy, which poses a great challenge to the speed, accuracy, and effect on the dose distribution of the networks ( 29 , 30 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

Li,

Song,

et al. 2023

Front. Oncol.

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PurposeImage segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.MethodsForty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.ResultsIn geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).ConclusionsCNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

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“…constructed an automatic pipeline from tumor segmentation to outcome prediction using pretreatment MRI. U-Net with a codec structure was used for segmentation, and a three-layer CNN was used to build the prediction models and achieve a DSC segmentation accuracy of 0.79, complete clinical response (cCR) prediction accuracy of 0.789, specificity of 0.725 and sensitivity of 0.812 [ 94 ]. With the recent introduction of total neoadjuvant therapy for rectal cancer and the ability to watch and wait for surgery in cCR cases, prognostication in this area is expected to become even more important in the future.…”

Section: Delivering the Planmentioning

confidence: 99%

Revolutionizing radiation therapy: the role of AI in clinical practice

Kawamura,

Kamomae,

Yanagawa

et al. 2023

Journal of Radiation Research

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This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist’s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

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Accurate tumor segmentation and treatment outcome prediction with DeepTOP

Cited by 7 publications

References 26 publications

Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer

Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

Revolutionizing radiation therapy: the role of AI in clinical practice

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