ClinicalTrials.gg
RecruitingNCT06527027Updated Dec 30, 2025

LN-RADS, RECIST 1.1 and Node-RADS Classification in the Assessment of Lymph Nodes

An interventional study of Lymph node assessment according to RECIST 1.1 in CT and Lymph node assessment according to Node-RADS in CT in Lymph Node Neoplasm and Lymph Node Metastasis, sponsored by Copernicus Memorial Hospital. Recruiting at 6 sites in Poland. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-12-30.

Sponsored by Copernicus Memorial Hospital · Not applicable, Interventional, and Diagnostic

From the registry’s dates

  • Registered 7 months after the study started (first participant enrolled Dec 2023, registered Jul 2024).
  • Started Dec 2023; still recruiting 2 years 11 months later.
Phase
Not applicable
Study type
Interventional
Enrollment
1,000
Allocation
Randomized
Ages
18 Years and older
Sex
All
01

Study summary

The project aims to evaluate the value of the new LN-RADS scales for lymph node classification in CT and MR and to compare this method with two other methods RECIST 1.1 and Node-RADS.

The main tested system in the study is LN-RADS, the comparators are RECIST 1.1 and Node-RADS criteria.

Lymph nodes are a key diagnostic and therapeutic element in oncology. Despite the technological progress, the detection of neoplastic changes in the lymph nodes is of low effectiveness, which results from the imperfection of the criteria used. Currently, the most widely used criterion is the RECIST 1.1 guideline developed in the 1990s, according to which the lymph node dimension in the short axis with a cut-off point of 10 mm is decisive. Lymph nodes smaller than 10 mm across are considered normal. It is a criterion with a high error rate, both due to the false-negative diagnoses (with small metastases below 10 mm) and false-positive diagnoses (in the case of inflammatory lymphadenopathy).

A particular disadvantageous situation is when the metastatic nodes and their transverse dimension is less than 10 mm, because they are treated as healthy nodes and the degree of the disease advancement is underestimated. As a result, the patient is not treated properly - no complete lymphadenectomy, no radiotherapy to the area of these nodes or insufficient systemic treatment. In all cases, underestimating the stage of the neoplastic diseases increases the risk of the recurrence.

LN-RADS accounts small metastases in nodes about 3 mm in size, thus about 20% more metastatic nodes may be detected compared to RECIST 1.1 method. This means that currently, according to RECIST 1.1 rules, approx. 20% of patients have missed nodal metastases and consequently receive insufficient treatment resulting in relapse. Previous studies have shown that RECIST 1.1 shows a high level of underestimation of metastatic nodes. The Node-RADS system, as the second comparator next to RECIT 1.1, is a fairly new system moving towards the structural assessment of lymph nodes, but proposed arbitrarily, without hard evidence for its effectiveness. Despite the publication of the Node-RADS system in a medical journal, it is not validated. The Node-RADS has numerous limitations and weaknesses that reduce its value.

02

Conditions studied

  • Lymph Node Neoplasm
  • Lymph Node Metastasis

Keywords

  • Lymph Nodes
  • Lymph Node Excision
  • Tomography, X-ray computed
  • Lymphadenopathy
  • Recurrence
  • Magnetic Resonance Imaging
03

In context

Lymphatic Metastasis

213 studies on the registry are indexed under Lymphatic Metastasis; 77 are open to participants now.

This study's planned enrollment of 1,000 is above the median of 97 across 109 interventional studies indexed under Lymphatic Metastasis.

Browse Lymphatic Metastasis studies →

Lead sponsor

Copernicus Memorial Hospital is the lead sponsor of 5 studies on the registry; 5 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • diagnosed or suspected cancer,
  • planned lymph node or lymphadenectomy,
  • planned or performed CT/MRI covering an area of the body with lymph nodes, - verified histopathologically or cytologically,
  • to participate in the study.

Exclusion criteria

Exclusion Criteria:

  • non-diagnostic CT/MRI images of lymph nodes due to reasons such as movement artifacts, artifacts from metal elements and any other factors that do not allow for proper assessment of the nodes,
  • inconclusive histopathological or cytological results, which do not allow the nodes to be classified into one of two groups - benign or malignant.
Terms in these criteria, in plain language(2)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Biopsy and archival tissue

A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.

Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Double (Participant, Investigator)
Enrollment
1,000 participants (estimated)

Study arms

  • Sham comparator
    Lymph node assessment according to RECIST 1.1

    Other: Lymph node assessment according to RECIST 1.1 in CT · Other: Lymph node assessment according to RECIST 1.1 in MRI

  • Experimental
    Lymph node assessment according to LN-RADS

    Other: Lymph node assessment according to LN-RADS in CT · Other: Lymph node assessment according to LN-RADS in MRI

  • Experimental
    Lymph node assessment according to Node-RADS

    Other: Lymph node assessment according to Node-RADS in CT · Other: Lymph node assessment according to Node-RADS in MRI

Interventions

  • OtherLymph node assessment according to RECIST 1.1 in CT

    RECIST 1.1 classifies lymph nodes as healthy when they have a short axis dimension (SAD) of \<10 mm; Nodes with a SAD dimension \>=10 mm are considered to be involved in the cancer process.

  • OtherLymph node assessment according to Node-RADS in CT

    Node-RADS classifies lymph nodes taking into account parameters such as: size, degree of homogeneity, boundaries and shape of the node. Depending on the degree of change in a given parameter, an appropriate number of points are awarded in each category, and the sum of the points determines the final classification of the node into one of five categories of probability of being affected by a cancer process: 1-very low, 2-low, 3-medium, 4 -high, 5-very high.

  • OtherLymph node assessment according to LN-RADS in CT

    LN-RADS (Lymph Node Reporting and Data System) categorizes nodes according to a scale that reflects the radiological and clinical forms of the nodes and the level of probability of a malignant process: LN-RADS 1 - normal lymph node LN-RADS 2 - enlarged and fatty lymph node, not suspected from an oncological point of view LN-RADS 3 - lymph node with features suggesting reactive changes. LN-RADS 4a - lymph node with slight oncological suspicion LN-RADS 4b - lymph node with strong oncological suspicion LN-RADS 5 - definitely cancerous node

  • OtherLymph node assessment according to RECIST 1.1 in MRI

    RECIST 1.1 classifies lymph nodes as healthy when they have a short axis dimension (SAD) of \<10 mm; Nodes with a SAD dimension \>=10 mm are considered to be involved in the cancer process.

  • OtherLymph node assessment according to Node-RADS in MRI

    Node-RADS classifies lymph nodes taking into account parameters such as: size, degree of homogeneity, boundaries and shape of the node. Depending on the degree of change in a given parameter, an appropriate number of points are awarded in each category, and the sum of the points determines the final classification of the node into one of five categories of probability of being affected by a cancer process: 1-very low, 2-low, 3-medium, 4 -high, 5-very high.

  • OtherLymph node assessment according to LN-RADS in MRI

    LN-RADS (Lymph Node Reporting and Data System) categorizes nodes according to a scale that reflects the radiological and clinical forms of the nodes and the level of probability of a malignant process: LN-RADS 1 - normal lymph node LN-RADS 2 - enlarged and fatty lymph node, not suspected from an oncological point of view LN-RADS 3 - lymph node with features suggesting reactive changes. LN-RADS 4a - lymph node with slight oncological suspicion LN-RADS 4b - lymph node with strong oncological suspicion LN-RADS 5 - definitely cancerous node

06

What researchers measure

Primary outcomes

  1. The effectiveness of assessment of LN-RADS, Node-RADS and RECIST 1.1

    Comparative assessment of the effectiveness of each of the three tested diagnostic methods (LN-RADS, RECIST 1.1, Node-RADS) in the form of an assessment of sensitivity, specificity and predictive value (positive/negative).

    Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)

Secondary outcomes

  1. Quantification of the agreement between raters assessing according to the specific classification system

    Estimation of the level of agreement between investigators in individual lymph node staging systems.

    Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)

  2. The predictive value of various morphological parameters of lymph nodes regarding in context of clinical characteristics

    Assessment of the predictive value of various morphological parameters of lymph nodes and clinical information in the context of differentiating benign and cancerous nodes.

    Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)

07

Study locations

1 of 6 sites recruiting
  • Maria Skłodowska-Curie National Research Institute of Oncology - National Research Institute
    Krakow, Poland
    Active, not recruiting

    Not enrolling at this site.

  • Copernicus Memorial Hospital
    Lodz, 93-513, Poland
    Recruiting
    • Cezary Chudobiński, PhD · Contactcezary.chudobinski@wp.pl · +4842 689 58 99
    • Cezary Chudobiński, PhD · Principal investigator
  • Independent Public Healthcare Centre (SPZOZ) , University Clinical Hospital No. 2 of the Medical University of Łódź
    Lodz, Poland
    Active, not recruiting

    Not enrolling at this site.

  • Doradztwo i Zarządzanie w Opiece Zdrowotnej A.K. Sp.z o.o
    Warsaw, Poland
    Active, not recruiting

    Not enrolling at this site.

  • Maria Skłodowska-Curie National Research Institute of Oncology - National Research Institute
    Warsaw, Poland
    Active, not recruiting

    Not enrolling at this site.

  • Professor Orłowski Hospital in Warsaw , Independent Public Healthcare Centre
    Warsaw, Poland
    Active, not recruiting

    Not enrolling at this site.

08

References and documents

Publications

  • Elsholtz FHJ, Asbach P, Haas M, Becker M, Beets-Tan RGH, Thoeny HC, Padhani AR, Hamm B. Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer. Eur Radiol. 2021 Aug;31(8):6116-6124. doi: 10.1007/s00330-020-07572-4. Epub 2021 Feb 14. PubMed 33585994 ↗
  • Prenzel KL, Monig SP, Sinning JM, Baldus SE, Brochhagen HG, Schneider PM, Holscher AH. Lymph node size and metastatic infiltration in non-small cell lung cancer. Chest. 2003 Feb;123(2):463-7. doi: 10.1378/chest.123.2.463. PubMed 12576367 ↗
  • Yoshimura G, Sakurai T, Oura S, Suzuma T, Tamaki T, Umemura T, Kokawa Y, Yang Q. Evaluation of Axillary Lymph Node Status in Breast Cancer with MRI. Breast Cancer. 1999 Jul 25;6(3):249-258. doi: 10.1007/BF02967179. PubMed 11091725 ↗
  • Choi YJ, Ko EY, Han BK, Shin JH, Kang SS, Hahn SY. High-resolution ultrasonographic features of axillary lymph node metastasis in patients with breast cancer. Breast. 2009 Apr;18(2):119-22. doi: 10.1016/j.breast.2009.02.004. Epub 2009 Mar 17. PubMed 19297159 ↗
  • Huvos AG, Hutter RV, Berg JW. Significance of axillary macrometastases and micrometastases in mammary cancer. Ann Surg. 1971 Jan;173(1):44-6. doi: 10.1097/00000658-197101000-00006. No abstract available. PubMed 5543548 ↗
  • LEBORGNE R, LEBORGNE F Jr, LEBORGNE JH. SOFT-TISSUE RADIOGRAPHY OF AXILLARY NODES WITH FATTY INFILTRATION. Radiology. 1965 Mar;84:513-5. doi: 10.1148/84.3.513. No abstract available. PubMed 14280727 ↗
  • Ahuja A, Ying M. An overview of neck node sonography. Invest Radiol. 2002 Jun;37(6):333-42. doi: 10.1097/00004424-200206000-00005. PubMed 12021590 ↗
  • Chikui T, Yonetsu K, Nakamura T. Multivariate feature analysis of sonographic findings of metastatic cervical lymph nodes: contribution of blood flow features revealed by power Doppler sonography for predicting metastasis. AJNR Am J Neuroradiol. 2000 Mar;21(3):561-7. PubMed 10730652 ↗
  • Rubaltelli L, Proto E, Salmaso R, Bortoletto P, Candiani F, Cagol P. Sonography of abnormal lymph nodes in vitro: correlation of sonographic and histologic findings. AJR Am J Roentgenol. 1990 Dec;155(6):1241-4. doi: 10.2214/ajr.155.6.2122673. PubMed 2122673 ↗
  • Woolgar JA, Rogers SN, Lowe D, Brown JS, Vaughan ED. Cervical lymph node metastasis in oral cancer: the importance of even microscopic extracapsular spread. Oral Oncol. 2003 Feb;39(2):130-7. doi: 10.1016/s1368-8375(02)00030-1. PubMed 12509965 ↗
  • Chudobinski C, Swiderski B, Antoniuk I, Kurek J. Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale. Cancers (Basel). 2024 Apr 19;16(8):1564. doi: 10.3390/cancers16081564. PubMed 38672646 ↗

Related links

Study documents

PDFs hosted by the registry, ClinicalTrials.gov.

Individual participant data

Plan to share: No

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Dec 30, 2025, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06527027
Lead sponsor
Copernicus Memorial Hospital
Collaborators
Medical Research Agency, Poland
Responsible party
Cezary Chudoniński (Principal Investigator, Copernicus Memorial Hospital) — Principal investigator
First posted
Jul 30, 2024
Start date
Dec 1, 2023
Primary completion
Dec 31, 2028 (estimated)
Completion
Dec 31, 2028 (estimated)
Last update
Dec 30, 2025

Study contacts

Cezary Chudobiński, PhD
Contact
cezary.chudobinski@wp.pl
+4842 689 58 99
Cezary Chudobiński, PhD
principal investigator · Copernicus Memoriał Hospital

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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