| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Liu, Han Hou, Chun‑Jie Wei, Min Lu, Ke‑Feng Liu, Ying Du, Pei Sun, Li‑Tao Tang, Jing‑Lan |
| Abstract | Background This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits. Methods A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study. Patients were randomly divided into training and internal testing group at an 8:2 ratio. Region of interest (ROI) was manually outlined, and habitat analysis subregions were defined using the K-means method. The ideal number of subregions (n = 5) was determined using the Calinski-Harabasz score, leading to the creation of a habitat radiomics model with 5 subregions and the identification of the high-risk habitat model. Area under the curve (AUC) values were calculated for all models to assess their validity, and predictive model nomograms were created by integrating clinical features. The internal and external testing dataset is employed to assess the predictive performance and stability of the model. Results In internal testing group, Habitat 3 was identified as the high-risk habitat model in the study, showing the best diagnostic efficacy among all models (AUC(CRM) vs. AUC(Habitat 3) vs. AUC(CRM + Habitat 3) = 0.84(95%CI:0.71–0.97) vs. 0.90(95%CI:0.80-1.00) vs. 0.79(95%CI:0.65–0.93)). Moreover, integrating the Habitat 3 model with clinical features and constructing nomograms enhanced the predictive capability of the combined model (AUC = 0.95(95%CI:0.88-1.00)). In this study, an independent external testing cohort was utilized to assess the model’s accuracy, yielding an AUC of 0.88 (95%CI: 0.78–0.98). Conclusion The integration of the High-Risk Habitats (Habitat 3) radiomics model with clinical characteristics demonstrated a high predictive accuracy in identifying LLNM. This model has the potential to offer valuable guidance to surgeons in deciding the necessity of LLNM dissection for DTC. Clinical trial number Not applicable. |
| Related Links | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-025-01551-1.pdf |
| Ending Page | 13 |
| Page Count | 13 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712342 |
| DOI | 10.1186/s12880-025-01551-1 |
| Journal | BMC Medical Imaging |
| Issue Number | 1 |
| Volume Number | 25 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2025-01-13 |
| Access Restriction | Open |
| Subject Keyword | Imaging Radiology Ultrasound Differentiated thyroid cancer Radiomics Habitat analysis Lateral cervical lymph node metastasis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 2.8/2023 |
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