Different approaches to volume assessment of lymph nodes in computer tomography scans of head and neck squamous cell carcinoma in comparison with a real gold standard

Mueller, Stefan; Wichmann, Gunnar; Dornheim, Lars; Roessling, Ivo; Bertolini, Julia; Preim, Bernhard; Dietz, Andreas; Boehm, Andreas

doi:10.1111/j.1445-2197.2012.06238.x

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…For LN algorithm development, 2D approaches have been published based on the measurement of the axial SAD as used in the classification systems mentioned above [ 29 , 30 ]. However, Mueller et al [ 31 ] found that in the head and neck region, the 2D segmentation method tends to overestimate the LN volume. Using a 3D approach based on the perimeter, they found a closer correlation to the true LN volume determined after neck dissection.…”

Section: Discussionmentioning

confidence: 99%

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

Rinneburger,

Carolus,

Iuga

et al. 2023

Eur Radiol Exp

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Background In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. Methods In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. Results In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. Conclusions Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. Relevance statement Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. Key points • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

Rinneburger,

Carolus,

Iuga

et al. 2023

Eur Radiol Exp

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“…The inherent technical difficulty also results in a threshold of 'minimally measurable disease', below which early stage patients cannot be reliably assessed and may not therefore be treated or offered entry to trials. 7 Volumetric tumour measurement is established in lung 8 and other cancers [9][10][11] and eliminates decision-making about where to make, and how to replicate, unidimensional measures on serial scans. Volumetric measurements based on MRI have recently been shown to outperform traditional T-staging in predicting survival, 12 but MRI is not available routinely and CT remains the primary imaging modality used in MPM.…”

Section: Pleural Diseasementioning

confidence: 99%

“…Volumetric tumour measurement is established in lung8 and other cancers9–11 and eliminates decision-making about where to make, and how to replicate, unidimensional measures on serial scans. Volumetric measurements based on MRI have recently been shown to outperform traditional T-staging in predicting survival,12 but MRI is not available routinely and CT remains the primary imaging modality used in MPM.…”

Section: Introductionmentioning

confidence: 99%

Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria

Kidd

Anderson

Cowell

et al. 2022

Thorax

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BackgroundIn malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.MethodsIn a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen’s kappa. Survival was assessed using Kaplan-Meier methodology.ResultsHuman and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival.ConclusionWe have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.

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“…Because it can be assumed that the volume of cancer reflects the number of cancer cells, the tumor volume should influence the production of mNS and the amount excreted and appearing in urine. Since the TNM categories (13) of the patients with HNSCC were available and some of the HNSCC in an accompanying study underwent volume assessment via computed tomography-based volumetry via image segmentation (14), the total volume of the malignant lesions was calculated after estimation of the volumes of the primary HNSCC (T) and potentially present locoregional lymph node metastases (N) and distant metastases (M).…”

Section: Determination Of Mnsmentioning

confidence: 99%

Modified Nucleosides – Molecular Markers Suitable for Small-volume Cancer?

Opitz¹,

Herbarth²,

Seidel³

et al. 2018

Anticancer Res

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Different approaches to volume assessment of lymph nodes in computer tomography scans of head and neck squamous cell carcinoma in comparison with a real gold standard

Abstract: The data show a higher reliability for volumes estimated by the segmentation-based approach than the widely used diameter-based approach.

Cited by 6 publications

References 17 publications

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria

Modified Nucleosides – Molecular Markers Suitable for Small-volume Cancer?

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