Differentiating lymph node status in malignant melanoma: the role of apparent diffusion coefficient ratio in diffusion-weighted MRI – a prospective diagnostic study

This study aimed to investigate a standardized apparent diffusion coefficient (ADC) ratio cut-off for identifying lymph node (LN) metastasis in malignant melanoma (MM), addressing the challenges of current non-invasive LN status diagnostics and dependences of absolute ADC values. This prospective, single-center study evaluated consecutive patients using diffusion-weighted MRI (DWI-MRI). We included 52 patients with early-stage MM who underwent sentinel lymph node (SLN) extraction, and 12 patients with advanced-stage MM and newly confirmed or progressive metastatic lymph nodes (MLNs). ADC values for positive and negative SLNs as well as MLNs were measured. Ratios of the ADC of SLNs or MLNs were calculated relative to benign contralateral LNs (cADC) and adjacent muscle tissue (mADC). ROC analysis identified cut-offs for absolute ADC values and relative ADC ratios. Diagnostic performance gained by regression was validated via machine learning (ML) classifiers, to evaluate robustness. A total of 64 patients (median age 68.5; IQR 61–77; 46.9% female) were included. Significant differences were observed in all ADC measurements using single-shot-echo-planar-imaging (EPI): median cADC was 0.76 (IQR 0.69–0.93) for metastatic SLNs versus 1.02 (IQR 0.91–1.08) for benign SLNs (p = 0.03), mADC was 0.48 (IQR 0.45–0.53) versus 0.61 (IQR 0.56–0.71, p = 0.001). Optimal cut-offs for cADC and mADC in the SLN group were 0.81 (AUC 0.75; 95%-CI 0.47-1) and 0.49 (AUC 0.9; 95%-CI 0.78-1). These closely matched the optimal cut-offs found in the MLN set: cADC 0.83 (AUC 0.98; 95%-CI 0.96-1) and mADC 0.49 (AUC 0.99; 95%-CI 0.98-1). In both sets, these ratios outperformed morphological criteria, such as short-axis diameter (AUC 0.61; 0.38–0.84 in SLN- and 0.86; 0.8–0.92 in MLN set) and Node-RADS (AUC 0.5; 0.27–0.72 and 0.89; 0.84–0.94). ML models reproduced the diagnostic patterns observed in logistic regression. The introduction of a standardized ADC ratio cut-off in DW-MRI to differentiate benign from metastatic lymph nodes in MM offers a valuable, normalized, non-invasive addition to traditional diagnostics, though the findings require confirmation in larger, multicenter and multi-scanner cohorts.

Authors

Institutions

Publication Details

Journal
BMC Medical Imaging
Published
2026-02-04
DOI
https://doi.org/10.1186/s12880-026-02205-6
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Differentiating lymph node status in malignant melanoma: the role of apparent diffusion coefficient ratio in diffusion-weighted MRI – a prospective diagnostic study

Fiona Mankertz, Carolin Malsch, Michael J. Kirsch, Julie Gamain et al.
BMC Medical Imaging
Cutaneous Melanoma Detection and Management
article

Differentiating lymph node status in malignant melanoma: the role of apparent diffusion coefficient ratio in diffusion-weighted MRI – a prospective diagnostic study

Fiona Mankertz, Carolin Malsch, Michael J. Kirsch, Julie Gamain, Kristin Peters, M Mühler, Silvia Ribback, M. L. Kromrey, Stine Lutze, Marc Philip Elias Kohnen, Chia-Jung Busch, Andreas Arnold
article en

Abstract

This study aimed to investigate a standardized apparent diffusion coefficient (ADC) ratio cut-off for identifying lymph node (LN) metastasis in malignant melanoma (MM), addressing the challenges of current non-invasive LN status diagnostics and dependences of absolute ADC values. This prospective, single-center study evaluated consecutive patients using diffusion-weighted MRI (DWI-MRI). We included 52 patients with early-stage MM who underwent sentinel lymph node (SLN) extraction, and 12 patients with advanced-stage MM and newly confirmed or progressive metastatic lymph nodes (MLNs). ADC values for positive and negative SLNs as well as MLNs were measured. Ratios of the ADC of SLNs or MLNs were calculated relative to benign contralateral LNs (cADC) and adjacent muscle tissue (mADC). ROC analysis identified cut-offs for absolute ADC values and relative ADC ratios. Diagnostic performance gained by regression was validated via machine learning (ML) classifiers, to evaluate robustness. A total of 64 patients (median age 68.5; IQR 61–77; 46.9% female) were included. Significant differences were observed in all ADC measurements using single-shot-echo-planar-imaging (EPI): median cADC was 0.76 (IQR 0.69–0.93) for metastatic SLNs versus 1.02 (IQR 0.91–1.08) for benign SLNs (p = 0.03), mADC was 0.48 (IQR 0.45–0.53) versus 0.61 (IQR 0.56–0.71, p = 0.001). Optimal cut-offs for cADC and mADC in the SLN group were 0.81 (AUC 0.75; 95%-CI 0.47-1) and 0.49 (AUC 0.9; 95%-CI 0.78-1). These closely matched the optimal cut-offs found in the MLN set: cADC 0.83 (AUC 0.98; 95%-CI 0.96-1) and mADC 0.49 (AUC 0.99; 95%-CI 0.98-1). In both sets, these ratios outperformed morphological criteria, such as short-axis diameter (AUC 0.61; 0.38–0.84 in SLN- and 0.86; 0.8–0.92 in MLN set) and Node-RADS (AUC 0.5; 0.27–0.72 and 0.89; 0.84–0.94). ML models reproduced the diagnostic patterns observed in logistic regression. The introduction of a standardized ADC ratio cut-off in DW-MRI to differentiate benign from metastatic lymph nodes in MM offers a valuable, normalized, non-invasive addition to traditional diagnostics, though the findings require confirmation in larger, multicenter and multi-scanner cohorts.

BMC Medical ImagingVol. 26(1)
Friedrich-Loeffler-Institut (DE), Universitätsmedizin Greifswald (DE), Universität Greifswald (DE), Micromun (DE), University Hospital Carl Gustav Carus (DE)
Quality Education
Openalex Percentile: Top 67%
Cutaneous Melanoma Detection and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.