Saturday, June 15, 2024

Radiomics in Predicting Lymph Node Metastasis of Lung Adenocarcinoma

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The integration of radiomics in assessing thoracic lymph node metastasis (LNM) in lung adenocarcinoma is gaining traction. A comprehensive review of the diagnostic performance of radiomic features derived from primary tumors to predict LNM had not been undertaken until now. This study aims to fill that gap by evaluating the methodological quality and diagnostic efficacy of radiomic approaches in this context.

Study Methodology

Researchers sifted through databases including PubMed, Embase, the Web of Science Core Collection, and the Cochrane library to gather relevant studies. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were used to evaluate the quality of the studies. Key metrics such as pooled sensitivity, specificity, and area under the curve (AUC) were calculated for the best radiomic models in both training and validation cohorts. Additionally, subgroup and meta-regression analyses were performed to enhance the study’s robustness.

Results and Findings

Seventeen studies involving 159 to 1202 patients each, published between 2018 and 2022, were included. Ten studies provided sufficient data for quantitative evaluation. The RQS percentages ranged from 11.1% to 44.4%, indicating variable quality. Most studies exhibited a low risk of bias and minimal applicability issues as per the QUADAS-2 assessment. Pyradiomics software and logistic regression analysis were predominantly used for feature extraction and selection. The best prediction models combined radiomic features with non-radiomic features such as semantic and clinical features.

The pooled sensitivity, specificity, and AUC for the training cohorts were 0.84, 0.88, and 0.93, respectively. For the validation cohorts, these metrics were 0.89, 0.86, and 0.94, respectively, reflecting high diagnostic accuracy.

Actionable Insights for Clinicians

  • Radiomic features from primary tumors show promise in preoperatively predicting LNM in lung adenocarcinoma.
  • Combining radiomic features with clinical and semantic data enhances predictive accuracy.
  • Standardizing radiomics workflows could further improve clinical applicability and reliability.

Radiomic features derived from primary tumors demonstrate significant potential for predicting preoperative LNM in lung adenocarcinoma. However, standardizing the radiomics workflow is crucial to enhance its applicability and reliability in clinical settings.

Original Article: BMC Pulm Med. 2024 May 18;24(1):246. doi: 10.1186/s12890-024-03020-x.

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