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Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study. | LitMetric

AI Article Synopsis

  • Cervical lymph node metastasis (LNM) is a key prognostic factor in non-small cell lung cancer (NSCLC) patients, and this study aimed to create machine learning models to improve its diagnosis using ultrasound imaging.
  • The research involved 313 NSCLC patients who underwent neck ultrasounds, extracting radiomic features and analyzing them with logistic regression (LR) and random forest (RF) models.
  • Results indicated that the combined model of semantic and radiomic features achieved the highest accuracy, highlighting the effectiveness of these machine learning approaches in diagnosing cervical LNM.

Article Abstract

Background: Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC.

Methods: This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively.

Results: In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968).

Conclusions: The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11055367PMC
http://dx.doi.org/10.1186/s12885-024-12306-6DOI Listing

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