Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study

Abstract

Purpose To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA).

Materials and Methods This study analyzed 280 biopsy-confirmed metastatic CLAs from 280 cancer patients, including 54 from head and neck squamous cell carcinoma (HNSCC), 58 from thyroid cancer (TC), 92 from lung cancer (LC), and 76 from gastrointestinal cancer (GIC). Before biopsy, patients underwent conventional ultrasound (CUS), ultrasound elastography (UE), and contrast-enhanced ultrasound (CEUS). Based on CUS, DLR models using CUS, CUS+UE, CUS+CEUS, and CUS+UE+CEUS data were developed and compared. The best model was integrated with key clinical indicators selected by univariate analysis to achieve the best classification performance.

Results All DLR models achieved similar performance with respect to classifying four primary tumor sites of metastatic CLA (AUC:0.708~0.755). After integrating key clinical indicators (age, sex, and neck level), the US+UE+CEUS+clinical model yielded the best performance with an overall AUC of 0.822 in the validation cohort, but there was no significance compared with the basal CUS+clinical model (P>0.05), both of which identified metastasis from HNSCC, TC, LC, and GIC with 0.869 and 0.911, 0.838 and 0.916, 0.750 and 0.610, and 0.829 and 0.769, respectively.

Conclusion The ultrasound-based DLR model can be used to classify the primary cancer sites of metastatic CLA, and the CUS combined with clinical indicators is adequate to provide a high discriminatory performance. The addition of the combination of UE and CEUS data is expected to further improve performance.

Zusammenfassung

Ziel Untersuchung der Machbarkeit von Deep-Learning-Radiomics (DLR), basierend auf multimodalem Ultraschall zur Differenzierung primärer Krebsherde bei metastasierter zervikaler Lymphadenopathie (CLA).

Material und Methoden In dieser Studie wurden 280 durch Biopsie bestätigte metastatische CLAs von 280 Krebspatienten analysiert, darunter 54 mit Plattenepithel-Karzinomen im Kopf-Hals-Bereich (HNSCC), 58 mit Schilddrüsenkrebs (TC), 92 mit Lungenkrebs (LC) und 76 mit Magen-Darm-Krebs (GIC). Vor der Biopsie wurden die Patienten einer konventionellen Ultraschalluntersuchung (CUS), einer Ultraschall-Elastografie (UE) und einer kontrastmittelverstärkten Ultraschalluntersuchung (CEUS) unterzogen. Basierend auf der CUS wurden DLR-Modelle unter Verwendung von Daten aus CUS, CUS + UE, CUS + CEUS und CUS + UE + CEUS entwickelt und verglichen. Das beste Modell wurde mit wichtigen klinischen, durch eine univariate Analyse ausgewählten Indikatoren kombiniert, um die beste Klassifizierungsleistung zu erzielen.

Ergebnisse Alle DLR-Modelle erreichten eine ähnliche Leistung bei der Klassifizierung von 4 Primärtumorstellen der metastasierten CLA (AUC: 0,708–0,755). Nach Integration wichtiger klinischer Indikatoren (Alter, Geschlecht und Halshöhe) erzielte das „US + UE + CEUS + klinische“ Modell die beste Leistung mit einer Gesamt-AUC von 0,822 in der Validierungskohorte. Im Vergleich zum basalen CUS + klinischen Modell war der Unterschied jedoch nicht signifikant (P>0,05). Bei beiden Modellen betrug die Leistung für die Identifikation von Metastasen bei HNSCC jeweils 0,869 bzw. 0,911; bei TC 0,838 und 0,916; bei LC 0,750 und 0,610 und bei GIC 0,829 und 0,769.

Schlussfolgerung Das ultraschallbasierte DLR-Modell kann zur Klassifizierung der primären Krebsherde der metastasierten CLA verwendet werden, und CUS in Kombination mit klinischen Indikatoren ist ausreichend, um eine hohe Trennschärfe zu erzielen. Man darf erwarten, dass mit der Kombination von UE- und CEUS-Daten die Leistung weiter verbessert wird.

Keywords

Multimodal Ultrasound - Metastatic Cervical Lymphadenopathy - Deep Learning - Primary Cancer Sites

Publication History

Received: 23 May 2023

Accepted after revision: 15 August 2023

Article published online:
05 December 2023

• References

• 1 Pynnonen MA, Gillespie MB, Roman B. et al. Clinical Practice Guideline: Evaluation of the Neck Mass in Adults. Otolaryngol Head Neck Surg 2017; 157: S1-s30 DOI: 10.1177/0194599817722550. (PMID: 28891406)
• 2 Bandoh N, Goto T, Akahane T. et al. Diagnostic value of liquid-based cytology with fine needle aspiration specimens for cervical lymphadenopathy. Diagn Cytopathol 2016; 44: 169-176
• 3 Pusztaszeri MP, Faquin WC. Cytologic evaluation of cervical lymph node metastases from cancers of unknown primary origin. Semin Diagn Pathol 2015; 32: 32-41 DOI: 10.1053/j.semdp.2014.12.002. (PMID: 25618223)
• 4 Kapoor D, Handa U, Kundu R. et al. Diagnostic utility of p16 immunocytochemistry in metastatic cervical lymph nodes in head and neck cancers. Diagn Cytopathol 2021; 49: 469-474 DOI: 10.1002/dc.24696. (PMID: 33428334)
• 5 Rettenbacher T. Sonography of peripheral lymph nodes part 2: Doppler criteria and typical findings of distinct entities. Ultraschall Med 2014; 35: 10-27 DOI: 10.1055/s-0033-1355593. (PMID: 24338731)
• 6 Yin S, Cui Q, Wang S. et al. Analysis of Contrast-Enhanced Ultrasound Perfusion Patterns and Time-Intensity Curves for Metastatic Lymph Nodes From Lung Cancer: Preliminary Results. J Ultrasound Med 2018; 37: 385-395
• 7 Tan S, Miao LY, Cui LG. et al. Value of Shear Wave Elastography Versus Contrast-Enhanced Sonography for Differentiating Benign and Malignant Superficial Lymphadenopathy Unexplained by Conventional Sonography. J Ultrasound Med 2017; 36: 189-199 DOI: 10.7863/ultra.16.01014. (PMID: 27925679)
• 8 Zhang X, Zhang Y, Yu X. et al. Different Primary Sites of Hypopharyngeal Cancer Have Different Lymph Node Metastasis Patterns: A Retrospective Analysis From Multi-Center Data. Front Oncol 2021; 11: 727991
• 9 Kann BH, Hosny A, Aerts H. Artificial intelligence for clinical oncology. Cancer Cell 2021; 39: 916-927 DOI: 10.1016/j.ccell.2021.04.002. (PMID: 33930310)
• 10 Zhu Y, Meng Z, Fan X. et al. Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy. BMC Med 2022; 20: 269
• 11 Onoue K, Fujima N, Andreu-Arasa VC. et al. Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT. Am J Otolaryngol 2021; 42: 103026
• 12 Han F, Xu M, Xie T. et al. Efficacy of ultrasound-guided core needle biopsy in cervical lymphadenopathy: A retrospective study of 6,695 cases. Eur Radiol 2018; 28: 1809-1817 DOI: 10.1007/s00330-017-5116-1. (PMID: 29188372)
• 13 Meng Z, Zhu Y, Pang W. et al. MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy. IEEE Trans Med Imaging 2023; 42: 996-1008
• 14 He K, Zhang X, Ren S. et al. Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition: 2016; 2016 DOI: 10.1007/s10916-019-1457-4. (PMID: 31641865)
• 15 Tran D, Wang H, Torresani L. et al. A Closer Look at Spatiotemporal Convolutions for Action Recognition.
• 16 Vaswani A, Shazeer N, Parmar N. et al. Attention Is All You Need. arXiv. 2017
• 17 Liu H, Jing B, Han W. et al. A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma. J Med Syst 2019; 43: 59
• 18 Qian X, Pei J, Zheng H. et al. Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat Biomed Eng 2021; 5: 522-532
• 19 Wildeboer RR, Mannaerts CK, van Sloun RJG. et al. Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. Eur Radiol 2020; 30: 806-815 DOI: 10.1007/s00330-019-06436-w. (PMID: 31602512)
• 20 Yang L, Luo D, Li L. et al. Differentiation of malignant cervical lymphadenopathy by dual-energy CT: a preliminary analysis. Sci Rep 2016; 6: 31020 DOI: 10.1038/srep31020. (PMID: 27498560)
• 21 Mahoney EJ, Spiegel JH. Evaluation and management of malignant cervical lymphadenopathy with an unknown primary tumor. Otolaryngol Clin North Am 2005; 38: 87-97 DOI: 10.1016/j.otc.2004.10.014. (PMID: 15649501)
• 22 Zhang J, Jin J, Ai Y. et al. Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images. Eur Radiol 2021; 31: 1022-1028
• 23 Lu MY, Chen TY, Williamson DFK. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 2021; 594: 106-110 DOI: 10.1038/s41586-021-03512-4. (PMID: 33953404)
• 24 Rios Velazquez E, Parmar C, Liu Y. et al. Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. Cancer Res 2017; 77: 3922-3930