Quantitative vascular feature-based multimodality prediction model for multi-origin malignant cervical lymphadenopathy.

Background: The precise prediction of multi-origin malignant cervical lymphadenopathy is limited by the low inter-reader reproducibility of imaging interpretation, and a quantitative method to improve this aspect is lacking. This study aimed to develop and validate an artificial intelligence framework integrating quantitative vascular features for assessing cervical lymphadenopathy and explore its utility among radiologists.

Methods: For this retrospective study, a total of 21,298 ultrasound images of 10,649 cervical lymph nodes (LNs) from 10,386 patients and 2366 images of 1183 LNs from 1151 patients at the Sun Yat-sen University Cancer Center between January 2011 and July 2022 were used for model development and internal testing, respectively. For external model testing, we used 776 images of 388 LNs from 360 patients at the Chongqing University Cancer Hospital between January and December 2022. Quantitative features used to characterize the vascular distribution and degree of richness were fused with morphological and semantic features on B-mode and color Doppler ultrasound images to develop a dual-modality, multi-feature, fusion lymph node network (DMFLNN). Subsequently, the performance of DMFLNN was compared with that of six radiologists, and its auxiliary value was assessed in test cohorts.

Findings: DMFLNN achieved an area under the receiver operating characteristic curve (AUC) of 0.937 for the internal test cohort and 0.875 for the external test cohort. Using the internal test cohort with assistance from DMFLNN, the average AUC improved from 0.814 to 0.836 for senior radiologists ( = 0.00018), and from 0.778 to 0.847 for junior radiologists ( < 0.0001). Additionally, the average inter-radiologist agreement improved from fair to moderate (improvement in kappa: from 0.590 to 0.696 for senior radiologists; from 0.571 to 0.750 for junior radiologists). Similar trends were observed for the external test cohort. Moreover, the radiologists' average false-positive rate decreased by 3.8% and 9.8% for the internal and external test cohorts, respectively.

Interpretation: DMFLNN could improve radiologists' performance and potentially reduce unnecessary biopsies of cervical lymphadenopathy. However, further testing is warranted before its wide adoption in clinical practice.

Funding: The National Natural Science Foundation of China (82171955; 62371476; 82441027); the China Department of Science and Technology (2023YFE0204300); and the R&D project of Pazhou Lab (HuangPu) (2023K0606).