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

Official title

Comparison of the LN-RADS, RECIST 1.1 and Node-RADS Classification in the Assessment of Lymph Nodes in MRI and CT in Relation to Histopathological Results - a Prospective, Randomised Study

Overview

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.

Interventions

• Other Lymph 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.
• Other Lymph 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.
• Other Lymph 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 susp
• Other Lymph 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.
• Other Lymph 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.
• Other Lymph 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 susp

Primary outcome measures

• The effectiveness of assessment of LN-RADS, Node-RADS and RECIST 1.1 [Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)]

Secondary outcome measures (2)

• Quantification of the agreement between raters assessing according to the specific classification system [Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)]
• The predictive value of various morphological parameters of lymph nodes regarding in context of clinical characteristics [Time frame: After accomplished lymph node assessment according to classification system (up to 1 year)]

Eligibility criteria

Inclusion criteria

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

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Double blind

Primary purpose

Diagnostic

Study locations

Poland · 6 centers

• Maria Skłodowska-Curie National Research Institute of Oncology - National Research Institu — Krakow
• Copernicus Memorial Hospital — Lodz
• Independent Public Healthcare Centre (SPZOZ) , University Clinical Hospital No. 2 of the M — Lodz
• Doradztwo i Zarządzanie w Opiece Zdrowotnej A.K. Sp.z o.o — Warsaw
• Maria Skłodowska-Curie National Research Institute of Oncology - National Research Institu — Warsaw
• Professor Orłowski Hospital in Warsaw , Independent Public Healthcare Centre — Warsaw

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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 10730652

Identifiers

NCT: NCT06527027 · 2022/ABM/03/00027

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗