PET Lesion Segmentation Using Automated Iso-intensity Contouring in Head and Neck Cancer

Simon, Edmund; Fox, Timothy; Lee, Daniel; Waller, Anthony F.; Pantalone, P.; Jani, Ashesh B.

doi:10.1177/153303460900800401

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Although the median segmentation time of both readers was comparable, differences in distribution were found linked to slightly different patient groups and readers' experience. In addition, segmentations were performed with a manual approach and not using semi- or automatic PET or CT segmentation that can improve the objectivity of tumor volume measurements, e.g., in head and neck cancer [ 34 ]. Furthermore, the differences in the reading environments with different sources of interruption for the reading and segmentation task complicate direct, one-to-one comparisons.…”

Section: Discussionmentioning

confidence: 99%

Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer

Sexauer

Weikert

Mäder

et al. 2018

Contrast Media & Molecular Imaging

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Results of PET/CT examinations are communicated as text-based reports which are frequently not fully structured. Incomplete or missing staging information can be a significant source of staging and treatment errors. We compared standard text-based reports to a manual full 3D-segmentation-based approach with respect to TNM completeness and processing time. TNM information was extracted retrospectively from 395 reports. Moreover, the RIS time stamps of these reports were analyzed. 2995 lesions using a set of 41 classification labels (TNM features + location) were manually segmented on the corresponding image data. Information content and processing time of reports and segmentations were compared using descriptive statistics and modelling. The TNM/UICC stage was mentioned explicitly in only 6% (n=22) of the text-based reports. In 22% (n=86), information was incomplete, most frequently affecting T stage (19%, n=74), followed by N stage (6%, n=22) and M stage (2%, n=9). Full NSCLC-lesion segmentation required a median time of 13.3 min, while the median of the shortest estimator of the text-based reporting time (R1) was 18.1 min (p=0.01). Tumor stage (UICC I/II: 5.2 min, UICC III/IV: 20.3 min, p < 0.001), lesion size (p < 0.001), and lesion count (n=1: 4.4 min, n=12: 37.2 min, p < 0.001) correlated significantly with the segmentation time, but not with the estimators of text-based reporting time. Numerous text-based reports are lacking staging information. A segmentation-based reporting approach tailored to the staging task improves report quality with manageable processing time and helps to avoid erroneous therapy decisions based on incomplete reports. Furthermore, segmented data may be used for multimedia enhancement and automatization.

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Section: Discussionmentioning

confidence: 99%

Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer

Sexauer

Weikert

Mäder

et al. 2018

Contrast Media & Molecular Imaging

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“…Of all 2D 18 FDG-PET axial images, those with sufficient 18 FDG uptake in the pre-radiation acquisition were selected by SUV max > 1.5 excluding brain regions (22). Overall, 718 axial slices collected from 38 patients were used for neural network training, 233 axial slices from 13 patients were used for validation, and 230 axial slices from 13 patients were used for independent tests.…”

Section: Patient Data and Network Trainingmentioning

confidence: 99%

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

Lafata

Mowery

et al. 2022

Front. Oncol.

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PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.ConclusionThe developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

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“…Approach of contour analysis have been performed for some cancers at image levels. [53][54][55][56] As a first approximation, such contour boundaries can be used for defining molecular or other parameters, a select subset of which subsequently can be identified as key parameters. Adding additional layers within such as the vascular system, immune system, and energy production contours could add sophistication to the understanding of tumorigenesis.…”

Section: Identification Of Operation Parameters With Asymmetric Effectsmentioning

confidence: 99%

“…These diseases, such as cancer, may be more amenable to this analysis. Approach of contour analysis have been performed for some cancers at image levels 53–56. As a first approximation, such contour boundaries can be used for defining molecular or other parameters, a select subset of which subsequently can be identified as key parameters.…”

Section: Obstacles To Failure Analyses Approachmentioning

confidence: 99%

Toward failure analyses in systems biology

Bhattacharya

Gomes

Cebulla

2010

WIREs Mechanisms of Disease

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Parallels between designed and biological systems with respect to formal failure analyses have been presented. Failure analysis in designed systems depends on an identified, limited set of parameters or operation variables with high predictive value. In contrast, the biological systems pose problems in identification of operation variables and the identified variables may not be accurate predictors of failure. The difficulty in parameter identification is because of large numbers of components and the inability to envelope variables at each compartment or contour level. Contour level maps for biological systems are currently non-existent, and most failure models are based on very limited, unilateral operation variables (a mutant gene). Operation variable identification within each contour level will enhance failure analyses of complex biological systems.

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PET Lesion Segmentation Using Automated Iso-intensity Contouring in Head and Neck Cancer

Cited by 7 publications

References 22 publications

Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer

Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

Toward failure analyses in systems biology

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