Top

Journal of Imaging Informatics in Medicine

Published in:

Open Access 16-01-2024 | Metastasis

Development and Validation of a 3D Resnet Model for Prediction of Lymph Node Metastasis in Head and Neck Cancer Patients

Authors: Yi-Hui Lin, Chieh-Ting Lin, Ya-Han Chang, Yen-Yu Lin, Jen-Jee Chen, Chun-Rong Huang, Yu-Wei Hsu, Weir-Chiang You

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2024

Abstract

The accurate diagnosis and staging of lymph node metastasis (LNM) are crucial for determining the optimal treatment strategy for head and neck cancer patients. We aimed to develop a 3D Resnet model and investigate its prediction value in detecting LNM. This study enrolled 156 head and neck cancer patients and analyzed 342 lymph nodes segmented from surgical pathologic reports. The patients’ clinical and pathological data related to the primary tumor site and clinical and pathology T and N stages were collected. To predict LNM, we developed a dual-pathway 3D Resnet model incorporating two Resnet models with different depths to extract features from the input data. To assess the model’s performance, we compared its predictions with those of radiologists in a test dataset comprising 38 patients. The study found that the dimensions and volume of LNM + were significantly larger than those of LNM-. Specifically, the Y and Z dimensions showed the highest sensitivity of 84.6% and specificity of 72.2%, respectively, in predicting LNM + . The analysis of various variations of the proposed 3D Resnet model demonstrated that Dual-3D-Resnet models with a depth of 34 achieved the highest AUC values of 0.9294. In the validation test of 38 patients and 86 lymph nodes dataset, the 3D Resnet model outperformed both physical examination and radiologists in terms of sensitivity (80.8% compared to 50.0% and 91.7%, respectively), specificity(90.0% compared to 88.5% and 65.4%, respectively), and positive predictive value (77.8% compared to 66.7% and 55.0%, respectively) in detecting individual LNM + . These results suggest that the 3D Resnet model can be valuable for accurately identifying LNM + in head and neck cancer patients. A prospective trial is needed to evaluate further the role of the 3D Resnet model in determining LNM + in head and neck cancer patients and its impact on treatment strategies and patient outcomes.

Available only for authorised users

Krestan C, Herneth AM, Formanek M, Czerny C: Modern imaging lymph node staging of the head and neck region. Eur J Radiol 58:360–366, 2006CrossRefPubMed

Mehanna H, Wong W-L, McConkey CC, Rahman JK, Robinson M, Hartley AGJ, Nutting C, Powell N, Al-Booz H, Robinson M, Junor E, Rizwanullah M, von Zeidler SV, Wieshmann H, Hulme C, Smith AF, Hall P, Dunn J: PET-CT Surveillance versus Neck Dissection in Advanced Head and Neck Cancer. N Engl J Med 374:1444–1454, 2016CrossRefPubMed

Foust AM, Ali RM, Nguyen XV, Agrawal A, Prevedello LM, Bourekas EC, Boulter DJ: Dual-Energy CT-Derived Iodine Content and Spectral Attenuation Analysis of Metastatic versus Nonmetastatic Lymph Nodes in Squamous Cell Carcinoma of the Oropharynx. Tomography 4:66–71, 2018CrossRefPubMedPubMedCentral

Mahmood S, Mair M, Fagiry R, Ahmed MM, Menon I, Ibrahim N, Baker A, Vaidhyanath R: Diagnostic efficacy of combined CT and MRI in detecting nodal metastasis in patients with oral cancer. Oral Surg Oral Med Oral Pathol Oral Radiol 133:343–348, 2022CrossRefPubMed

Horváth A, Prekopp P, Polony G, Székely E, Tamás L, Dános K: Accuracy of the preoperative diagnostic workup in patients with head and neck cancers undergoing neck dissection in terms of nodal metastases. Eur Arch Otorhinolaryngol 278:2041–2046, 2021CrossRefPubMed

Fraggetta F, Garozzo S, Zannoni GF, Pantanowitz L, Rossi ED: Routine Digital Pathology Workflow: The Catania Experience. J Pathol Inform 8:51, 2017CrossRefPubMedPubMedCentral

Bassani S, Santonicco N, Eccher A, Scarpa A, Vianini M, Brunelli M, Bisi N, Nocini R, Sacchetto L, Munari E, Pantanowitz L, Girolami I, Molteni G: Artificial intelligence in head and neck cancer diagnosis. J Pathol Inform 13:100153, 2022. https://doi.org/10.1016/j.jpi.2022.100153

Caldonazzi N, Rizzo PC, Eccher A, Girolami I, Fanelli GN, Naccarato AG, Bonizzi G, Fusco N, d’Amati G, Scarpa A, Pantanowitz L, Marletta S (2023) Value of Artificial Intelligence in Evaluating Lymph Node Metastases. Cancers (Basel). https://doi.org/10.3390/cancers15092491, Apr 26, 2023

Yuan Y, Ren J, Tao X: Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol 31:6429–6437, 2021CrossRefPubMed

10.

Bogowicz M, Tanadini-Lang S, Guckenberger M, Riesterer O: Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer. Sci Rep 9:1–7, 2019CrossRef

11.

Lohmann P, Bousabarah K, Hoevels M, Treuer H: Radiomics in radiation oncology—basics, methods, and limitations. Strahlenther Onkol 196:848–855, 2020CrossRefPubMedPubMedCentral

12.

Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90, 2017CrossRef

13.

Zhou Z, Chen L, Sher D, Zhang Q, Shah J, Pham N-L, Jiang S, Wang J: Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimentional Convolutional Neural Network through Evidential Reasoning. Conf Proc IEEE Eng Med Biol Soc 2018:1–4, 2018

14.

Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E: Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol 127:458–463, 2019CrossRefPubMed

15.

Tomita H, Yamashiro T, Heianna J, Nakasone T, Kimura Y, Mimura H, Murayama S: Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography. Eur Radiol 31:7440–7449, 2021CrossRefPubMed

16.

Simonyan K, Zisserman A: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556, September 4, 2014

17.

He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. arXiv. https://doi.org/10.48550/arXiv.1512.03385, December 10, 2015

18.

Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M: A Closer Look at Spatiotemporal Convolutions for Action Recognition. arXiv. https://doi.org/10.48550/arXiv.1711.11248, November 30, 2017

19.

Hara K, Kataoka H, Satoh Y: Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? arXiv. https://doi.org/10.48550/arXiv.1711.09577, November 27, 2017

20.

Zhang X, Han L, Zhu W, Sun L, Zhang D: An Explainable 3D Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis Using Structural MRI. IEEE J Biomed Health Inform 26:5289–5297, 2022CrossRefPubMed

21.

Shim E, Kim JY, Yoon JP, Ki S-Y, Lho T, Kim Y, Chung SW: Automated rotator cuff tear classification using 3D convolutional neural network. Sci Rep 10:15632, 2020CrossRefPubMedPubMedCentral

22.

Hong J, Huang Y, Ye J, Wang J, Xu X, Wu Y, Li Y, Zhao J, Li R, Kang J, Lai X: 3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2022.912283, May 13, 2022

23.

Breau RH, Clark E, Bruner B, Cervini P, Atwell T, Knoll G, Leibovich BC: A simple method to estimate renal volume from computed tomography. Can Urol Assoc J 7:189–192, 2013CrossRefPubMedPubMedCentral

24.

Eder-Czembirek C, Erlacher B, Thurnher D, Erovic BM, Selzer E, Formanek M: Comparative Analysis of Clinical and Pathological Lymph Node Staging Data in Head and Neck Squamous Cell Carcinoma Patients Treated at the General Hospital Vienna. Radiol Oncol 52:173–180, 2018CrossRefPubMedPubMedCentral

25.

Castelijns JA, van den Brekel MW: Detection of lymph node metastases in the neck: radiologic criteria. AJNR Am J Neuroradiol 22:3–4, 2001PubMedPubMedCentral

26.

van den Brekel MW, Castelijns JA, Snow GB: Detection of lymph node metastases in the neck: radiologic criteria. Radiology 192:617–618, 1994CrossRefPubMed

27.

Ng S-H, Yen T-C, Liao C-T, Chang JT-C, Chan S-C, Ko S-F, Wang H-M, Wong H-F: 18F-FDG PET and CT/MRI in oral cavity squamous cell carcinoma: a prospective study of 124 patients with histologic correlation. J Nucl Med 46:1136–1143, 2005PubMed

28.

McGuirt WF, Williams DW 3rd, Keyes JW Jr, Greven KM, Watson NE Jr, Geisinger KR, Cappellari JO: A comparative diagnostic study of head and neck nodal metastases using positron emission tomography. Laryngoscope 105:373–375, 1995CrossRefPubMed

29.

Hao SP, Ng SH: Magnetic resonance imaging versus clinical palpation in evaluating cervical metastasis from head and neck cancer. Otolaryngol Head Neck Surg 123:324–327, 2000CrossRefPubMed

30.

Piao Y, Bold B, Tayier A, Ishida R, Omura K, Okada N, Shibuya H: Evaluation of 18F-FDG PET/CT for diagnosing cervical nodal metastases in patients with oral cavity or oropharynx carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 108:933–938, 2009CrossRefPubMed

31.

Liao C-T, Wang H-M, Huang S-F, Chen I-H, Kang C-J, Lin C-Y, Fan K-H, Ng S-H, Hsueh C, Lee L-Y, Lin C-H, Yen T-C: PET and PET/CT of the neck lymph nodes improves risk prediction in patients with squamous cell carcinoma of the oral cavity. J Nucl Med 52:180–187, 2011CrossRefPubMed

32.

Mochizuki Y, Omura K, Nakamura S, Harada H, Shibuya H, Kurabayashi T: Preoperative predictive model of cervical lymph node metastasis combining fluorine-18 fluorodeoxyglucose positron-emission tomography/computerized tomography findings and clinical factors in patients with oral or oropharyngeal squamous cell carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol 113:274–282, 2012CrossRefPubMed

33.

Davis AT, Palmer AL, Nisbet A: Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review. Br J Radiol 90:20160406, 2017CrossRefPubMed

34.

Stieb S, McDonald B, Gronberg M, Engeseth GM, He R, Fuller CD: Imaging for Target Delineation and Treatment Planning in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am 33:963–975, 2019CrossRefPubMedPubMedCentral

35.

Grégoire V, Coche E, Cosnard G, Hamoir M, Reychler H: Selection and delineation of lymph node target volumes in head and neck conformal radiotherapy. Proposal for standardizing terminology and procedure based on the surgical experience. Radiother Oncol 56:135–150, 2000

36.

Van den Bosch L, van der Schaaf A, van der Laan HP, Hoebers FJP, Wijers OB, van den Hoek JGM, Moons KGM, Reitsma JB, Steenbakkers RJHM, Schuit E, Langendijk JA: Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: A new concept for individually optimised treatment. Radiother Oncol 157:147–154, 2021CrossRefPubMed

37.

Grégoire V, Ang K, Budach W, Grau C, Hamoir M, Langendijk JA, Lee A, Le Q-T, Maingon P, Nutting C, O’Sullivan B, Porceddu SV, Lengele B: Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines. Radiother Oncol 110:172–181, 2014

38.

Grégoire V, Langendijk JA, Nuyts S: Advances in Radiotherapy for Head and Neck Cancer. J Clin Oncol 33:3277–3284, 2015CrossRefPubMed

Title: Development and Validation of a 3D Resnet Model for Prediction of Lymph Node Metastasis in Head and Neck Cancer Patients
Authors: Yi-Hui Lin
Chieh-Ting Lin
Ya-Han Chang
Yen-Yu Lin
Jen-Jee Chen
Chun-Rong Huang
Yu-Wei Hsu
Weir-Chiang You
Publication date: 16-01-2024
Publisher: Springer International Publishing
Keywords: Metastasis
Computed Tomography
Computed Tomography
Published in: Journal of Imaging Informatics in Medicine / Issue 2/2024
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-023-00938-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Development and Validation of a 3D Resnet Model for Prediction of Lymph Node Metastasis in Head and Neck Cancer Patients

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 2/2024

Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study

A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks

Development of Local Software for Automatic Measurement of Geometric Parameters in the Proximal Femur Using a Combination of a Deep Learning Approach and an Active Shape Model on X-ray Images

Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease

Robustness of Deep Networks for Mammography: Replication Across Public Datasets